diff --git a/README.md b/README.md
index ae9e9db..be7cd2b 100644
--- a/README.md
+++ b/README.md
@@ -25,7 +25,10 @@ pip install pywhyllm
 PyWhy-LLM seamlessly integrates into your existing causal inference process. Import the necessary classes and start exploring the power of LLM-augmented causal analysis.
 
 ```python
-from pywhyllm import ModelSuggester, IdentificationSuggester, ValidationSuggester
+from pywhyllm.suggesters.model_suggester import ModelSuggester 
+from pywhyllm.suggesters.identification_suggester import IdentificationSuggester
+from pywhyllm.suggesters.validation_suggester import ValidationSuggester
+from pywhyllm import RelationshipStrategy
 
 ```
 
@@ -34,17 +37,20 @@ from pywhyllm import ModelSuggester, IdentificationSuggester, ValidationSuggeste
 
 ```python
 # Create instance of Modeler
-modeler = Modeler()
+modeler = ModelSuggester('gpt-4')
+
+all_factors = ["smoking", "lung cancer", "exercise habits", "air pollution exposure"]
+treatment = "smoking"
+outcome = "lung cancer"
+
+# Suggest a list of domain expertises
+domain_expertises = modeler.suggest_domain_expertises(all_factors)
 
 # Suggest a set of potential confounders
-suggested_confounders = modeler.suggest_confounders(variables=_variables, treatment=treatment, outcome=outcome, llm=gpt4)
+suggested_confounders = modeler.suggest_confounders(treatment, outcome, all_factors, domain_expertises)
 
 # Suggest pair-wise relationship between variables
-suggested_dag = modeler.suggest_relationships(variables=selected_variables, llm=gpt4)
-
-plt.figure(figsize=(10, 5))
-nx.draw(suggested_dag, with_labels=True, node_color='lightblue')
-plt.show()
+suggested_dag = modeler.suggest_relationships(treatment, outcome, all_factors, domain_expertises, RelationshipStrategy.Pairwise)
 ```
 
 
@@ -54,15 +60,13 @@ plt.show()
 
 ```python
 # Create instance of Identifier
-identifier = Identifier()
+identifier = IdentificationSuggester('gpt-4')
 
-# Suggest a backdoor set, front door set, and iv set
-suggested_backdoor = identifier.suggest_backdoor(llm=gpt4, treatment=treatment, outcome=outcome, confounders=suggested_confounders)
-suggested_frontdoor = identifier.suggest_frontdoor(llm=gpt4, treatment=treatment, outcome=outcome,  confounders=suggested_confounders)
-suggested_iv = identifier.suggest_iv(llm=gpt4, treatment=treatment, outcome=outcome,  confounders=suggested_confounders)
+# Suggest a backdoor set, mediator set, and iv set
+suggested_backdoor = identifier.suggest_backdoor(treatment, outcome, all_factors, domain_expertises)
+suggested_mediators = identifier.suggest_mediators(treatment, outcome, all_factors, domain_expertises)
+suggested_iv = identifier.suggest_ivs(treatment, outcome, all_factors, domain_expertises)
 
-# Suggest an estimand based on the suggester backdoor set, front door set, and iv set
-estimand = identifier.suggest_estimand(confounders=suggested_confounders, treatment=treatment, outcome=outcome, backdoor=suggested_backdoor, frontdoor=suggested_frontdoor, iv=suggested_iv, llm=gpt4)  
 ```
 
 
@@ -72,20 +76,16 @@ estimand = identifier.suggest_estimand(confounders=suggested_confounders, treatm
 
 ```python
 # Create instance of Validator
-validator = Validator()
+validator = ValidationSuggester('gpt-4')
 
-# Suggest a critique of the provided DAG
-suggested_critiques_dag = validator.critique_graph(graph=suggested_dag, llm=gpt4)
+# Suggest a critique of the edges in provided DAG
+suggested_critiques_dag = validator.critique_graph(all_factors, suggested_dag, domain_expertises, RelationshipStrategy.Pairwise)
 
 # Suggest latent confounders
-suggested_latent_confounders = validator.suggest_latent_confounders(treatment=treatment, outcome=outcome, llm=gpt4)
+suggested_latent_confounders = validator.suggest_latent_confounders(treatment, outcome, all_factors, domain_expertises)
 
 # Suggest negative controls
-suggested_negative_controls = validator.suggest_negative_controls(variables=selected_variables, treatment=treatment, outcome=outcome, llm=gpt4)
-
-plt.figure(figsize=(10, 5))
-nx.draw(suggested_critiques_dag, with_labels=True, node_color='lightblue')
-plt.show()
+suggested_negative_controls = validator.suggest_negative_controls(treatment, outcome, all_factors, domain_expertises)
 
 ```
 
diff --git a/pywhyllm/suggesters/identification_suggester.py b/pywhyllm/suggesters/identification_suggester.py
index 78a513a..5cd0d15 100644
--- a/pywhyllm/suggesters/identification_suggester.py
+++ b/pywhyllm/suggesters/identification_suggester.py
@@ -19,7 +19,7 @@ def __init__(self, llm=None):
     #     self,
     #     treatment: str,
     #     outcome: str,
-    #     factors_list: list(),
+    #     all_factors: list(),
     #     llm: guidance.models,
     #     backdoor: Set[str] = None,
     #     frontdoor: Set[str] = None,
@@ -41,7 +41,7 @@ def __init__(self, llm=None):
     #         backdoor_edges, backdoor_set = self.suggest_backdoor(
     #             treatment=treatment,
     #             outcome=outcome,
-    #             factors_list=factors_list,
+    #             all_factors=all_factors,
     #             llm=llm,
     #             experts=experts,
     #             analysis_context=analysis_context,
@@ -66,7 +66,7 @@ def __init__(self, llm=None):
     #         frontdoor_edges, frontdoor_set = self.suggest_frontdoor(
     #             treatment=treatment,
     #             outcome=outcome,
-    #             factors_list=factors_list,
+    #             all_factors=all_factors,
     #             llm=llm,
     #             experts=experts,
     #             analysis_context=analysis_context,
@@ -87,7 +87,7 @@ def __init__(self, llm=None):
     #         ivs_edges, ivs_set = self.suggest_ivs(
     #             treatment=treatment,
     #             outcome=outcome,
-    #             factors_list=factors_list,
+    #             all_factors=all_factors,
     #             llm=llm,
     #             experts=experts,
     #             analysis_context=analysis_context,
@@ -116,15 +116,15 @@ def suggest_backdoor(
             self,
             treatment: str,
             outcome: str,
-            factors_list: list(),
+            all_factors: list(),
             expertise_list: list(),
-            analysis_context=CONTEXT,
+            analysis_context: str = CONTEXT,
             stakeholders: list() = None
     ):
         backdoor_set = self.model_suggester.suggest_confounders(
             treatment=treatment,
             outcome=outcome,
-            factors_list=factors_list,
+            all_factors=all_factors,
             expertise_list=expertise_list,
             analysis_context=analysis_context,
             stakeholders=stakeholders
@@ -136,9 +136,9 @@ def suggest_frontdoor(
             self,
             treatment: str,
             outcome: str,
-            factors_list: list(),
+            all_factors: list(),
             expertise_list: list(),
-            analysis_context=CONTEXT,
+            analysis_context: str = CONTEXT,
             stakeholders: list() = None
     ):
         pass
@@ -147,9 +147,9 @@ def suggest_mediators(
             self,
             treatment: str,
             outcome: str,
-            factors_list: list(),
+            all_factors: list(),
             expertise_list: list(),
-            analysis_context=CONTEXT,
+            analysis_context: str = CONTEXT,
             stakeholders: list() = None
     ):
         expert_list: List[str] = list()
@@ -164,16 +164,16 @@ def suggest_mediators(
         mediators_edges[(treatment, outcome)] = 1
 
         edited_factors_list: List[str] = []
-        for i in range(len(factors_list)):
-            if factors_list[i] != treatment and factors_list[i] != outcome:
-                edited_factors_list.append(factors_list[i])
+        for i in range(len(all_factors)):
+            if all_factors[i] != treatment and all_factors[i] != outcome:
+                edited_factors_list.append(all_factors[i])
 
         for expert in expert_list:
             mediators_edges, mediators_list = self.request_mediators(
                 treatment=treatment,
                 outcome=outcome,
                 domain_expertise=expert,
-                factors_list=edited_factors_list,
+                all_factors=edited_factors_list,
                 mediators_edges=mediators_edges,
                 analysis_context=analysis_context
             )
@@ -187,9 +187,9 @@ def request_mediators(
             treatment,
             outcome,
             domain_expertise,
-            factors_list,
+            all_factors,
             mediators_edges,
-            analysis_context=CONTEXT
+            analysis_context: str = CONTEXT
     ):
         mediators: List[str] = list()
 
@@ -218,7 +218,7 @@ def request_mediators(
                  on the causal chain that links the {treatment} to the {outcome}? From your perspective as an expert in 
                  {domain_expertise}, which factor(s) of the following factors, if any at all, mediates, is/are on the causal 
                  chain, that links the {treatment} to the {outcome}? Then provide your step by step chain of thoughts within 
-                 the tags <thinking></thinking>. factor_names : {factors_list} Wrap the name of the factor(s), if any at all, 
+                 the tags <thinking></thinking>. factor_names : {all_factors} Wrap the name of the factor(s), if any at all, 
                  that has/have a high likelihood of directly influencing and causing the assignment of the {outcome} and also 
                  has/have a high likelihood of being directly influenced and caused by the assignment of the {treatment} within
                   the tags <mediating_factor>factor_name</mediating_factor>. Where factor_name is one of the items within the 
@@ -237,7 +237,7 @@ def request_mediators(
                 if mediating_factor:
                     for factor in mediating_factor:
                         # to not add it twice into the list
-                        if factor in factors_list and factor not in mediators:
+                        if factor in all_factors and factor not in mediators:
                             mediators.append(factor)
                 success = True
 
@@ -262,9 +262,9 @@ def suggest_ivs(
             self,
             treatment: str,
             outcome: str,
-            factors_list: list(),
+            all_factors: list(),
             expertise_list: list(),
-            analysis_context=CONTEXT,
+            analysis_context: str = CONTEXT,
             stakeholders: list() = None
     ):
         expert_list: List[str] = list()
@@ -279,9 +279,9 @@ def suggest_ivs(
         iv_edges[(treatment, outcome)] = 1
 
         edited_factors_list: List[str] = []
-        for i in range(len(factors_list)):
-            if factors_list[i] != treatment and factors_list[i] != outcome:
-                edited_factors_list.append(factors_list[i])
+        for i in range(len(all_factors)):
+            if all_factors[i] != treatment and all_factors[i] != outcome:
+                edited_factors_list.append(all_factors[i])
 
         for expert in expert_list:
             iv_edges, iv_list = self.request_ivs(
@@ -289,7 +289,7 @@ def suggest_ivs(
                 outcome=outcome,
                 analysis_context=analysis_context,
                 domain_expertise=expert,
-                factors_list=edited_factors_list,
+                all_factors=edited_factors_list,
                 iv_edges=iv_edges,
             )
 
@@ -305,7 +305,7 @@ def request_ivs(
             outcome,
             analysis_context,
             domain_expertise,
-            factors_list,
+            all_factors,
             iv_edges
     ):
         ivs: List[str] = list()
@@ -338,7 +338,7 @@ def request_ivs(
                        the {outcome}? Which factor(s) of the following factors, if any at all, are (an) instrumental variable(s) 
                        to the causal relationship of the {treatment} causing the {outcome}? Be concise and keep your thinking 
                        within two paragraphs. Then provide your step by step chain of thoughts within the tags 
-                       <thinking></thinking>. factor_names : {factors_list} Wrap the name of the factor(s), if there are any at 
+                       <thinking></thinking>. factor_names : {all_factors} Wrap the name of the factor(s), if there are any at 
                        all, that both has/have a high likelihood of influecing and causing the {treatment} and has/have a very low 
                        likelihood of influencing and causing the {outcome}, within the tags <iv_factor>factor_name</iv_factor>. 
                        Where factor_name is one of the items within the factor_names list. If a factor does not have a high 
@@ -353,7 +353,7 @@ def request_ivs(
 
                 if iv_factors:
                     for factor in iv_factors:
-                        if factor in factors_list and factor not in ivs:
+                        if factor in all_factors and factor not in ivs:
                             ivs.append(factor)
                 success = True
 
diff --git a/pywhyllm/suggesters/model_suggester.py b/pywhyllm/suggesters/model_suggester.py
index 7d5fc33..b7b9df2 100644
--- a/pywhyllm/suggesters/model_suggester.py
+++ b/pywhyllm/suggesters/model_suggester.py
@@ -17,9 +17,9 @@ def __init__(self, llm=None):
 
     def suggest_domain_expertises(
             self,
-            factors_list,
+            all_factors,
             n_experts: int = 1,
-            analysis_context=CONTEXT
+            analysis_context: str = CONTEXT
     ):
 
         expertise_list: List[str] = list()
@@ -34,7 +34,7 @@ def suggest_domain_expertises(
                 with user():
                     prompt_str = f"""What domain expertises have the knowledge and experience needed to identify causal 
                     relationships and causal influences between the {analysis_context}? What domain expertises are needed 
-                    to work with and reason about the causal influence between {factors_list}? What domain expertises 
+                    to work with and reason about the causal influence between {all_factors}? What domain expertises 
                     have the knowledge and experience to reason and answer questions about influence and cause between 
                     such factors? Think about this in a step by step manner and recommend {n_experts} expertises and 
                     provide each one wrapped within the tags, <domain_expertise></domain_expertise>, along with the 
@@ -61,9 +61,9 @@ def suggest_domain_expertises(
 
     def suggest_domain_experts(
             self,
-            factors_list,
+            all_factors,
             n_experts: int = 5,
-            analysis_context=CONTEXT
+            analysis_context: str = CONTEXT
     ):
 
         experts_list: Set[str] = set()
@@ -77,7 +77,7 @@ def suggest_domain_experts(
                 with user():
                     prompt_str = f"""What domain experts have the knowledge and experience needed to identify causal relationships 
                     and causal influences between the {analysis_context}? What experts are needed to work with and 
-                    reason about the causal influence between {factors_list}? What domain experts have the knowledge 
+                    reason about the causal influence between {all_factors}? What domain experts have the knowledge 
                     and experience to reason and answer questions about influence and cause between such factors? Think 
                     about this in a step by step manner and recommend {n_experts} domain experts and provide each one 
                     wrapped within the tags, <domain_expert></domain_expert>, along with the reasoning and explanation 
@@ -104,9 +104,9 @@ def suggest_domain_experts(
 
     def suggest_stakeholders(
             self,
-            factors_list,
+            all_factors,
             n_stakeholders: int = 5,  # must be > 1
-            analysis_context=CONTEXT
+            analysis_context: str = CONTEXT
     ):
 
         stakeholder_list: List[str] = list()
@@ -122,7 +122,7 @@ def suggest_stakeholders(
                 with user():
                     prompt_str = f"""What stakeholders have knowledge and experience in and about {analysis_context}? 
                 What stakeholders can work best with and reason well about the causal influence between 
-                {factors_list}? What stakeholders have the knowledge and experience useful to reason within this context? Think about 
+                {all_factors}? What stakeholders have the knowledge and experience useful to reason within this context? Think about 
                 this in a step by step manner and recommend {n_stakeholders} stakeholders. Then provide each useful stakeholder 
                 wrapped within the tags, <stakeholder></stakeholder>, along with the reasoning and explanation wrapped between the tags
                 <explanation></explanation>."""
@@ -152,7 +152,7 @@ def suggest_confounders(
             self,
             treatment: str,
             outcome: str,
-            factors_list: list,
+            all_factors: list,
             expertise_list: list,
             analysis_context: str = CONTEXT,
             stakeholders: list = None
@@ -170,9 +170,9 @@ def suggest_confounders(
         confounders: List[str] = list()
 
         edited_factors_list: List[str] = []
-        for i in range(len(factors_list)):
-            if factors_list[i] != treatment and factors_list[i] != outcome:
-                edited_factors_list.append(factors_list[i])
+        for i in range(len(all_factors)):
+            if all_factors[i] != treatment and all_factors[i] != outcome:
+                edited_factors_list.append(all_factors[i])
 
         for expert in expertise_list:
             confounders_edges, confounders_list = self.request_confounders(
@@ -180,7 +180,7 @@ def suggest_confounders(
                 outcome=outcome,
                 analysis_context=analysis_context,
                 domain_expertise=expert,
-                factors_list=edited_factors_list,
+                all_factors=edited_factors_list,
                 confounders_edges=confounders_edges
             )
 
@@ -195,9 +195,9 @@ def request_confounders(
             treatment,
             outcome,
             domain_expertise,
-            factors_list,
+            all_factors,
             confounders_edges,
-            analysis_context=CONTEXT
+            analysis_context: str = CONTEXT
     ):
         confounders: List[str] = list()
 
@@ -229,7 +229,7 @@ def request_confounders(
                 keep your 
                 thinking within two paragraphs. Then provide your step by step chain of thoughts within the tags 
                 <thinking></thinking>. \n factor_names : 
-                {factors_list} Wrap the name of the factor(s), if any at all, that has/have a high likelihood of directly influencing 
+                {all_factors} Wrap the name of the factor(s), if any at all, that has/have a high likelihood of directly influencing 
                 and causing both  the {treatment} and the {outcome}, within the tags 
                 <confounding_factor>factor_name</confounding_factor> where 
                 factor_name is one of the items within the factor_names list. If a factor does not have a high likelihood of directly 
@@ -244,7 +244,7 @@ def request_confounders(
                 if confounding_factors:
                     for factor in confounding_factors:
                         # to not add it twice into the list
-                        if factor in factors_list and factor not in confounders:
+                        if factor in all_factors and factor not in confounders:
                             confounders.append(factor)
                 success = True
 
@@ -269,14 +269,14 @@ def suggest_parents(
             self,
             domain_expertise,
             factor,
-            factors_list,
-            analysis_context=CONTEXT
+            all_factors,
+            analysis_context: str = CONTEXT
     ):
         parent_candidates: List[str] = []
 
-        for i in range(len(factors_list)):
-            if factors_list[i] != factor:
-                parent_candidates.append(factors_list[i])
+        for i in range(len(all_factors)):
+            if all_factors[i] != factor:
+                parent_candidates.append(all_factors[i])
 
         parents: List[str] = list()
 
@@ -303,7 +303,7 @@ def suggest_parents(
                             (2) From your perspective as an expert in {domain_expertise} which of the following 
                             factors has 
                             a high likelihood of directly influencing and causing the {factor}? factors list: [
-{factors_list}] 
+{all_factors}] 
                             For any factors within the list with a high likelihood of directly influencing and causing 
                             the {factor} wrap the name of the factor with the tags 
                             <influencing_factor>factor_name</influencing_factor>. 
@@ -335,15 +335,15 @@ def suggest_children(
             self,
             domain_expertise,
             factor,
-            factors_list,
-            analysis_context=CONTEXT
+            all_factors,
+            analysis_context: str = CONTEXT
     ):
 
         children_candidates: List[str] = []
 
-        for i in range(len(factors_list)):
-            if factors_list[i] != factor:
-                children_candidates.append(factors_list[i])
+        for i in range(len(all_factors)):
+            if all_factors[i] != factor:
+                children_candidates.append(all_factors[i])
 
         children: List[str] = list()
 
@@ -373,7 +373,7 @@ def suggest_children(
                             list, 
                             if any at all, has/have a high likelihood of being directly influenced and caused by the {factor}? What 
                             factor(
-                            s) from the factors list, if any at all, is/are affected by the {factor}? factors list: [{factors_list}] 
+                            s) from the factors list, if any at all, is/are affected by the {factor}? factors list: [{all_factors}] 
                             For 
                             any factors within the list with a high likelihood of being directly influenced and caused by the {factor}, 
                             wrap the name of the factor with the tags <influenced_factor>factor_name</influenced_factor>. If a factor 
@@ -467,9 +467,9 @@ def suggest_relationships(
             self,
             treatment: str,
             outcome: str,
-            factors_list: list,
+            all_factors: list,
             expertise_list: list,
-            relationship_strategy: RelationshipStrategy = RelationshipStrategy.Parent,
+            relationship_strategy: RelationshipStrategy = RelationshipStrategy.Pairwise,
             analysis_context: str = CONTEXT,
             stakeholders: list = None,
     ):
@@ -483,19 +483,19 @@ def suggest_relationships(
         if relationship_strategy == RelationshipStrategy.Parent:
             "loop asking parents program"
             parent_edges: Dict[Tuple[str, str], int] = dict()
-            for factor in factors_list:
+            for factor in all_factors:
                 for expert in expert_list:
                     suggested_parent = self.suggest_parents(
                         domain_expertise=expert,
                         factor=factor,
-                        factors_list=factors_list,
+                        all_factors=all_factors,
                         analysis_context=analysis_context
                     )
                     for element in suggested_parent:
                         if (
                                 element,
                                 factor,
-                        ) in parent_edges and element in factors_list:
+                        ) in parent_edges and element in all_factors:
                             parent_edges[(element, factor)] += 1
                         else:
                             parent_edges[(element, factor)] = 1
@@ -507,19 +507,19 @@ def suggest_relationships(
 
             children_edges: Dict[Tuple[str, str], int] = dict()
 
-            for factor in factors_list:
+            for factor in all_factors:
                 for expert in expert_list:
                     suggested_children = self.suggest_children(
                         domain_expertise=expert,
                         factor=factor,
-                        factors_list=factors_list,
+                        all_factors=all_factors,
                         analysis_context=analysis_context
                     )
                     for element in suggested_children:
                         if (
                                 element,
                                 factor,
-                        ) in children_edges and element in factors_list:
+                        ) in children_edges and element in all_factors:
                             children_edges[(element, factor)] += 1
                         else:
                             children_edges[(element, factor)] = 1
@@ -531,7 +531,7 @@ def suggest_relationships(
 
             pairwise_edges: Dict[Tuple[str, str], int] = dict()
 
-            for (factor_a, factor_b) in itertools.combinations(factors_list, 2):
+            for (factor_a, factor_b) in itertools.combinations(all_factors, 2):
                 for expert in expert_list:
                     suggested_edge = self.suggest_pairwise_relationship(
                         domain_expertise=expert,
@@ -554,7 +554,7 @@ def suggest_relationships(
             confounders_counter, confounders = self.suggest_confounders(
                 treatment=treatment,
                 outcome=outcome,
-                factors_list=factors_list,
+                all_factors=all_factors,
                 expertise_list=expertise_list,
                 analysis_context=analysis_context
             )
diff --git a/pywhyllm/suggesters/validation_suggester.py b/pywhyllm/suggesters/validation_suggester.py
index 337fbd7..dcd0cc5 100644
--- a/pywhyllm/suggesters/validation_suggester.py
+++ b/pywhyllm/suggesters/validation_suggester.py
@@ -23,9 +23,9 @@ def suggest_negative_controls(
             self,
             treatment: str,
             outcome: str,
-            factors_list: list(),
+            all_factors: list(),
             expertise_list: list(),
-            analysis_context=CONTEXT,
+            analysis_context: str = CONTEXT,
             stakeholders: list() = None
     ):
         expert_list: List[str] = list()
@@ -39,16 +39,16 @@ def suggest_negative_controls(
         negative_controls: List[str] = list()
 
         edited_factors_list: List[str] = []
-        for i in range(len(factors_list)):
-            if factors_list[i] != treatment and factors_list[i] != outcome:
-                edited_factors_list.append(factors_list[i])
+        for i in range(len(all_factors)):
+            if all_factors[i] != treatment and all_factors[i] != outcome:
+                edited_factors_list.append(all_factors[i])
 
         for expert in expert_list:
             negative_controls_counter,
             negative_controls_list = self.request_negative_controls(
                 treatment=treatment,
                 outcome=outcome,
-                factors_list=edited_factors_list,
+                all_factors=edited_factors_list,
                 negative_controls_counter=negative_controls_counter,
                 domain_expertise=expert,
                 analysis_context=analysis_context
@@ -63,10 +63,10 @@ def request_negative_controls(
             self,
             treatment: str,
             outcome: str,
-            factors_list: list(),
+            all_factors: list(),
             negative_controls_counter: list(),
             domain_expertise: str,
-            analysis_context=CONTEXT
+            analysis_context: str = CONTEXT
     ):
         negative_controls_list: List[str] = list()
 
@@ -84,7 +84,7 @@ def request_negative_controls(
                     lm += cleandoc(prompt_str)
 
                 with user():
-                    prompt_str = f"""factor_names: {factors_list} From your perspective as an expert in the {domain_expertise}, what factor(s) from the list of factors, relevant to 
+                    prompt_str = f"""factor_names: {all_factors} From your perspective as an expert in the {domain_expertise}, what factor(s) from the list of factors, relevant to 
                              the {analysis_context}, should see zero treatment effect when changing the {treatment}? Which factor(s) 
                              from the list of factors, if any at all, relevant to the {analysis_context}, are negative controls on the 
                              causal mechanisms that affect the {outcome} when changing {treatment}? Using your domain knowledge, 
@@ -113,7 +113,7 @@ def request_negative_controls(
                     for factor in negative_controls:
                         # to not add it twice into the list
                         if (
-                                factor in factors_list
+                                factor in all_factors
                                 and factor not in negative_controls_list
                         ):
                             negative_controls_list.append(factor)
@@ -135,7 +135,7 @@ def suggest_latent_confounders(
             treatment: str,
             outcome: str,
             expertise_list: list(),
-            analysis_context=CONTEXT,
+            analysis_context: str = CONTEXT,
             stakeholders: list() = None
     ):
         expert_list: List[str] = list()
@@ -169,7 +169,7 @@ def request_latent_confounders(
             outcome: str,
             latent_confounders_counter: list(),
             domain_expertise: str,
-            analysis_context=CONTEXT
+            analysis_context: str = CONTEXT
     ):
         latent_confounders_list: List[str] = list()
 
@@ -227,15 +227,15 @@ def request_latent_confounders(
     def request_parent_critique(
             self,
             factor,
-            factors_list,
+            all_factors,
             domain_expertise,
-            analysis_context=CONTEXT
+            analysis_context: str = CONTEXT
     ):
         edited_factors_list: List[str] = []
 
-        for i in range(len(factors_list)):
-            if factors_list[i] not in factor:
-                edited_factors_list.append(factors_list[i])
+        for i in range(len(all_factors)):
+            if all_factors[i] not in factor:
+                edited_factors_list.append(all_factors[i])
 
         parents: List[str] = list()
 
@@ -250,10 +250,10 @@ def request_parent_critique(
                     lm += cleandoc(prompt_str)
                 with user():
                     prompt_str = f"""Steps: (1) 
-                                Analyze potential factors [{factors_list}] for factors directly influencing/causing/affecting {
+                                Analyze potential factors [{all_factors}] for factors directly influencing/causing/affecting {
                     factor}. Is relationship direct? Ignore feedback mechanisms/factors not in list. Keep thoughts within 
                                 <thinking></thinking> tags. (2) Use prior thoughts to answer: how {factor} influenced/caused/affected by  [
-                                {factors_list}]? Is relationship direct? Ignore feedback mechanisms/factors not in list. Wrap 
+                                {all_factors}]? Is relationship direct? Ignore feedback mechanisms/factors not in list. Wrap 
                                 factors highly likely directly influencing/causing/affecting {factor} in 
                                 <influencing_factor></influencing_factor> tags. No tags for low likelihood factors. Ignore feedback 
                                 mechanisms/factors not in list. Answer as {domain_expertise} expert."""
@@ -280,15 +280,15 @@ def request_parent_critique(
     def request_children_critique(
             self,
             factor,
-            factors_list,
+            all_factors,
             domain_expertise,
-            analysis_context=CONTEXT
+            analysis_context: str = CONTEXT
     ):
         edited_factors_list: List[str] = []
 
-        for i in range(len(factors_list)):
-            if factors_list[i] not in factor:
-                edited_factors_list.append(factors_list[i])
+        for i in range(len(all_factors)):
+            if all_factors[i] not in factor:
+                edited_factors_list.append(all_factors[i])
 
         children: List[str] = list()
 
@@ -305,10 +305,10 @@ def request_children_critique(
 
                 with user():
                     prompt_str = f"""Steps: (
-                                1) Analyze potential factors [{factors_list}] for factors directly influenced/caused/affected by 
+                                1) Analyze potential factors [{all_factors}] for factors directly influenced/caused/affected by 
                                 {factor}. Is relationship direct? Ignore feedback mechanisms/factors not in list. Keep thoughts within 
                                 <thinking></thinking> tags. (2) Use prior thoughts to answer: how {factor} influences/causes/affects [{
-                    factors_list}]? Is relationship direct? Ignore feedback mechanisms/factors not in list. Wrap 
+                    all_factors}]? Is relationship direct? Ignore feedback mechanisms/factors not in list. Wrap 
                                 factors highly likely directly influenced/caused/affected by {factor} in 
                                 <influenced_factor></influenced_factor> tags. No tags for low likelihood factors. Ignore feedback 
                                 mechanisms/factors not in list. Answer as {domain_expertise} expert."""
@@ -389,10 +389,10 @@ def request_pairwise_critique(
 
     def critique_graph(
             self,
-            factors_list: List[str],
+            all_factors: List[str],
             edges: Dict[Tuple[str, str], int],
             experts: list(),
-            relationship_strategy: RelationshipStrategy = RelationshipStrategy.Parent,
+            relationship_strategy: RelationshipStrategy = RelationshipStrategy.Pairwise,
             analysis_context: str = CONTEXT,
             stakeholders: list() = None,
     ):
@@ -408,19 +408,19 @@ def critique_graph(
 
             parent_edges: Dict[Tuple[str, str], int] = dict()
 
-            for factor in factors_list:
+            for factor in all_factors:
                 for expert in expert_list:
                     suggested_parent = self.request_parent_critique(
                         analysis_context=analysis_context,
                         factor=factor,
-                        factors_list=factors_list,
+                        all_factors=all_factors,
                         domain_expertise=expert
                     )
                     for element in suggested_parent:
                         if (
                                 element,
                                 factor,
-                        ) in parent_edges and element in factors_list:
+                        ) in parent_edges and element in all_factors:
                             parent_edges[(element, factor)] += 1
                         else:
                             parent_edges[(element, factor)] = 1
@@ -432,16 +432,16 @@ def critique_graph(
 
             critiqued_children_edges: Dict[Tuple[str, str], int] = dict()
 
-            for factor in factors_list:
+            for factor in all_factors:
                 for expert in expert_list:
                     suggested_children = self.request_children_critique(
                         factor=factor,
-                        factors_list=factors_list,
+                        all_factors=all_factors,
                         domain_expertise=expert,
                         analysis_context=analysis_context
                     )
                     for element in suggested_children:
-                        if (element, factor) in critiqued_children_edges and element in factors_list:
+                        if (element, factor) in critiqued_children_edges and element in all_factors:
                             critiqued_children_edges[(element, factor)] += 1
                         else:
                             critiqued_children_edges[(element, factor)] = 1
@@ -453,7 +453,7 @@ def critique_graph(
 
             critiqued_pairwise_edges: Dict[Tuple[str, str], int] = dict()
 
-            for (factor_a, factor_b) in itertools.combinations(factors_list, 2):
+            for (factor_a, factor_b) in itertools.combinations(all_factors, 2):
                 for expert in expert_list:
                     suggested_edge = self.request_pairwise_critique(
                         factor_a=factor_a,
diff --git a/pywhyllm/tests/__init__.py b/tests/__init__.py
similarity index 100%
rename from pywhyllm/tests/__init__.py
rename to tests/__init__.py
diff --git a/pywhyllm/tests/model_suggester/__init__.py b/tests/model_suggester/__init__.py
similarity index 100%
rename from pywhyllm/tests/model_suggester/__init__.py
rename to tests/model_suggester/__init__.py
diff --git a/pywhyllm/tests/model_suggester/data_providers/__init__.py b/tests/model_suggester/data_providers/__init__.py
similarity index 100%
rename from pywhyllm/tests/model_suggester/data_providers/__init__.py
rename to tests/model_suggester/data_providers/__init__.py
diff --git a/pywhyllm/tests/model_suggester/data_providers/identification_suggester_data_provider.py b/tests/model_suggester/data_providers/identification_suggester_data_provider.py
similarity index 100%
rename from pywhyllm/tests/model_suggester/data_providers/identification_suggester_data_provider.py
rename to tests/model_suggester/data_providers/identification_suggester_data_provider.py
diff --git a/pywhyllm/tests/model_suggester/data_providers/model_suggester_data_provider.py b/tests/model_suggester/data_providers/model_suggester_data_provider.py
similarity index 100%
rename from pywhyllm/tests/model_suggester/data_providers/model_suggester_data_provider.py
rename to tests/model_suggester/data_providers/model_suggester_data_provider.py
diff --git a/pywhyllm/tests/model_suggester/data_providers/simple_identification_suggester_data_provider.py b/tests/model_suggester/data_providers/simple_identification_suggester_data_provider.py
similarity index 100%
rename from pywhyllm/tests/model_suggester/data_providers/simple_identification_suggester_data_provider.py
rename to tests/model_suggester/data_providers/simple_identification_suggester_data_provider.py
diff --git a/pywhyllm/tests/model_suggester/data_providers/simple_model_suggester_data_provider.py b/tests/model_suggester/data_providers/simple_model_suggester_data_provider.py
similarity index 100%
rename from pywhyllm/tests/model_suggester/data_providers/simple_model_suggester_data_provider.py
rename to tests/model_suggester/data_providers/simple_model_suggester_data_provider.py
diff --git a/pywhyllm/tests/model_suggester/data_providers/tuebingen_model_suggester_data_provider.py b/tests/model_suggester/data_providers/tuebingen_model_suggester_data_provider.py
similarity index 100%
rename from pywhyllm/tests/model_suggester/data_providers/tuebingen_model_suggester_data_provider.py
rename to tests/model_suggester/data_providers/tuebingen_model_suggester_data_provider.py
diff --git a/pywhyllm/tests/model_suggester/data_providers/validation_suggester_data_provider.py b/tests/model_suggester/data_providers/validation_suggester_data_provider.py
similarity index 100%
rename from pywhyllm/tests/model_suggester/data_providers/validation_suggester_data_provider.py
rename to tests/model_suggester/data_providers/validation_suggester_data_provider.py
diff --git a/pywhyllm/tests/model_suggester/test_identification_suggester.py b/tests/model_suggester/test_identification_suggester.py
similarity index 91%
rename from pywhyllm/tests/model_suggester/test_identification_suggester.py
rename to tests/model_suggester/test_identification_suggester.py
index 5515db4..d60203d 100644
--- a/pywhyllm/tests/model_suggester/test_identification_suggester.py
+++ b/tests/model_suggester/test_identification_suggester.py
@@ -4,8 +4,9 @@
 
 from pywhyllm.suggesters.identification_suggester import IdentificationSuggester
 from pywhyllm.suggesters.model_suggester import ModelSuggester
-from pywhyllm.tests.model_suggester.data_providers.model_suggester_data_provider import *
-from pywhyllm.tests.model_suggester.data_providers.identification_suggester_data_provider import *
+from tests.model_suggester.data_providers.identification_suggester_data_provider import *
+from tests.model_suggester.data_providers.model_suggester_data_provider import *
+
 
 class TestIdentificationSuggester(unittest.TestCase):
     def test_suggest_backdoor(self):
diff --git a/pywhyllm/tests/model_suggester/test_model_suggester.py b/tests/model_suggester/test_model_suggester.py
similarity index 98%
rename from pywhyllm/tests/model_suggester/test_model_suggester.py
rename to tests/model_suggester/test_model_suggester.py
index 98292fe..38c0ef6 100644
--- a/pywhyllm/tests/model_suggester/test_model_suggester.py
+++ b/tests/model_suggester/test_model_suggester.py
@@ -3,8 +3,8 @@
 from guidance.models._openai import OpenAI
 
 from pywhyllm.suggesters.model_suggester import ModelSuggester
-from pywhyllm.tests.model_suggester.data_providers.model_suggester_data_provider import *
 from pywhyllm.helpers import RelationshipStrategy
+from tests.model_suggester.data_providers.model_suggester_data_provider import *
 
 
 class TestModelSuggester(unittest.TestCase):
diff --git a/pywhyllm/tests/model_suggester/test_simple_identification_suggester.py b/tests/model_suggester/test_simple_identification_suggester.py
similarity index 93%
rename from pywhyllm/tests/model_suggester/test_simple_identification_suggester.py
rename to tests/model_suggester/test_simple_identification_suggester.py
index 2e87809..a7d91e3 100644
--- a/pywhyllm/tests/model_suggester/test_simple_identification_suggester.py
+++ b/tests/model_suggester/test_simple_identification_suggester.py
@@ -3,7 +3,7 @@
 from guidance.models._openai import OpenAI
 
 from pywhyllm.suggesters.simple_identification_suggester import SimpleIdentificationSuggester
-from pywhyllm.tests.model_suggester.data_providers.simple_identification_suggester_data_provider import *
+from tests.model_suggester.data_providers.simple_identification_suggester_data_provider import *
 
 class TestSimpleIdentificationSuggester(unittest.TestCase):
 
diff --git a/pywhyllm/tests/model_suggester/test_simple_model_suggester.py b/tests/model_suggester/test_simple_model_suggester.py
similarity index 97%
rename from pywhyllm/tests/model_suggester/test_simple_model_suggester.py
rename to tests/model_suggester/test_simple_model_suggester.py
index 70bd3ef..bfe5973 100644
--- a/pywhyllm/tests/model_suggester/test_simple_model_suggester.py
+++ b/tests/model_suggester/test_simple_model_suggester.py
@@ -3,7 +3,7 @@
 from guidance.models._openai import OpenAI
 
 from pywhyllm.suggesters.simple_model_suggester import SimpleModelSuggester
-from pywhyllm.tests.model_suggester.data_providers.simple_model_suggester_data_provider import *
+from tests.model_suggester.data_providers.simple_model_suggester_data_provider import *
 
 
 class TestSimpleModelSuggester(unittest.TestCase):
diff --git a/pywhyllm/tests/model_suggester/test_tuebingen_model_suggester.py b/tests/model_suggester/test_tuebingen_model_suggester.py
similarity index 97%
rename from pywhyllm/tests/model_suggester/test_tuebingen_model_suggester.py
rename to tests/model_suggester/test_tuebingen_model_suggester.py
index c90e308..9d2bb25 100644
--- a/pywhyllm/tests/model_suggester/test_tuebingen_model_suggester.py
+++ b/tests/model_suggester/test_tuebingen_model_suggester.py
@@ -3,8 +3,7 @@
 from guidance.models._openai import OpenAI
 
 from pywhyllm.suggesters.tuebingen_model_suggester import TuebingenModelSuggester, Strategy
-from pywhyllm.tests.model_suggester.data_providers.tuebingen_model_suggester_data_provider import *
-
+from tests.model_suggester.data_providers.tuebingen_model_suggester_data_provider import *
 
 class TestTuebingenModelSuggester(unittest.TestCase):
     def test_suggest_description(self):
diff --git a/pywhyllm/tests/model_suggester/test_validation_suggester.py b/tests/model_suggester/test_validation_suggester.py
similarity index 96%
rename from pywhyllm/tests/model_suggester/test_validation_suggester.py
rename to tests/model_suggester/test_validation_suggester.py
index dd3a014..106ec5b 100644
--- a/pywhyllm/tests/model_suggester/test_validation_suggester.py
+++ b/tests/model_suggester/test_validation_suggester.py
@@ -4,10 +4,9 @@
 from guidance.models._openai import OpenAI
 
 from pywhyllm.suggesters.validation_suggester import ValidationSuggester
-from pywhyllm.tests.model_suggester.data_providers.validation_suggester_data_provider import *
-from pywhyllm.tests.model_suggester.data_providers.model_suggester_data_provider import *
 from pywhyllm.helpers import RelationshipStrategy
-
+from tests.model_suggester.data_providers.model_suggester_data_provider import *
+from tests.model_suggester.data_providers.validation_suggester_data_provider import *
 
 class TestValidationSuggester(unittest.TestCase):
     def test_suggest_latent_confounders(self):
diff --git a/pywhyllm/tests/test_notebooks.py b/tests/test_notebooks.py
similarity index 100%
rename from pywhyllm/tests/test_notebooks.py
rename to tests/test_notebooks.py