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causalinference.html
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causalinference.html
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<article id="content">
<header>
<h1 class="title">Module <code>ktrain.tabular.causalinference</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def causal_inference_model(
df,
method="t-learner",
metalearner_type=None,
treatment_col="treatment",
outcome_col="outcome",
text_col=None,
ignore_cols=[],
include_cols=[],
treatment_effect_col="treatment_effect",
learner=None,
effect_learner=None,
min_df=0.05,
max_df=0.5,
ngram_range=(1, 1),
stop_words="english",
verbose=1,
):
"""
```
Infers causality from the data contained in `df` using a metalearner.
This function is a wrapper to the CausalNLP.CausalInferenceModel class.
For more details on methods and capabilities of the returned `CausalInferenceModel` object,
see the [CausalNLP documentation](https://amaiya.github.io/causalnlp/causalinference.html).
Usage:
>>> cm = causal_inference_model(df,
treatment_col='Is_Male?',
outcome_col='Post_Shared?', text_col='Post_Text',
ignore_cols=['id', 'email'])
cm.fit()
**Parameters:**
* **df** : pandas.DataFrame containing dataset
* **method** : metalearner model to use. One of {'t-learner', 's-learner', 'x-learner', 'r-learner'} (Default: 't-learner')
* **metalearner_type** : Alias of **method** parameter for backwards compatibility. If not None, overrides method.
* **treatment_col** : treatment variable; column should contain binary values: 1 for treated, 0 for untreated.
* **outcome_col** : outcome variable; column should contain the categorical or numeric outcome values
* **text_col** : (optional) text column containing the strings (e.g., articles, reviews, emails).
* **ignore_cols** : columns to ignore in the analysis
* **include_cols** : columns to include as covariates (e.g., possible confounders)
* **treatment_effect_col** : name of column to hold causal effect estimations. Does not need to exist. Created by CausalNLP.
* **learner** : an instance of a custom learner. If None, a default LightGBM will be used.
# Example
learner = LGBMClassifier(num_leaves=1000)
* **effect_learner**: used for x-learner/r-learner and must be regression model
* **min_df** : min_df parameter used for text processing using sklearn
* **max_df** : max_df parameter used for text procesing using sklearn
* **ngram_range**: ngrams used for text vectorization. default: (1,1)
* **stop_words** : stop words used for text processing (from sklearn)
* **verbose** : If 1, print informational messages. If 0, suppress.
**Returns:**
`CausalNLP.CausalInferenceModel` object
```
"""
try:
import causalnlp
except ImportError:
raise Exception("CausalNLP must be installed: pip install causalnlp")
from causalnlp import CausalInferenceModel
return CausalInferenceModel(
df,
method=method,
metalearner_type=metalearner_type,
treatment_col=treatment_col,
outcome_col=outcome_col,
text_col=text_col,
ignore_cols=ignore_cols,
include_cols=include_cols,
treatment_effect_col=treatment_effect_col,
learner=learner,
effect_learner=effect_learner,
min_df=min_df,
max_df=max_df,
ngram_range=ngram_range,
stop_words=stop_words,
verbose=verbose,
)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="ktrain.tabular.causalinference.causal_inference_model"><code class="name flex">
<span>def <span class="ident">causal_inference_model</span></span>(<span>df, method='t-learner', metalearner_type=None, treatment_col='treatment', outcome_col='outcome', text_col=None, ignore_cols=[], include_cols=[], treatment_effect_col='treatment_effect', learner=None, effect_learner=None, min_df=0.05, max_df=0.5, ngram_range=(1, 1), stop_words='english', verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Infers causality from the data contained in `df` using a metalearner.
This function is a wrapper to the CausalNLP.CausalInferenceModel class.
For more details on methods and capabilities of the returned `CausalInferenceModel` object,
see the [CausalNLP documentation](https://amaiya.github.io/causalnlp/causalinference.html).
Usage:
>>> cm = causal_inference_model(df,
treatment_col='Is_Male?',
outcome_col='Post_Shared?', text_col='Post_Text',
ignore_cols=['id', 'email'])
cm.fit()
**Parameters:**
* **df** : pandas.DataFrame containing dataset
* **method** : metalearner model to use. One of {'t-learner', 's-learner', 'x-learner', 'r-learner'} (Default: 't-learner')
* **metalearner_type** : Alias of **method** parameter for backwards compatibility. If not None, overrides method.
* **treatment_col** : treatment variable; column should contain binary values: 1 for treated, 0 for untreated.
* **outcome_col** : outcome variable; column should contain the categorical or numeric outcome values
* **text_col** : (optional) text column containing the strings (e.g., articles, reviews, emails).
* **ignore_cols** : columns to ignore in the analysis
* **include_cols** : columns to include as covariates (e.g., possible confounders)
* **treatment_effect_col** : name of column to hold causal effect estimations. Does not need to exist. Created by CausalNLP.
* **learner** : an instance of a custom learner. If None, a default LightGBM will be used.
# Example
learner = LGBMClassifier(num_leaves=1000)
* **effect_learner**: used for x-learner/r-learner and must be regression model
* **min_df** : min_df parameter used for text processing using sklearn
* **max_df** : max_df parameter used for text procesing using sklearn
* **ngram_range**: ngrams used for text vectorization. default: (1,1)
* **stop_words** : stop words used for text processing (from sklearn)
* **verbose** : If 1, print informational messages. If 0, suppress.
**Returns:**
`CausalNLP.CausalInferenceModel` object
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def causal_inference_model(
df,
method="t-learner",
metalearner_type=None,
treatment_col="treatment",
outcome_col="outcome",
text_col=None,
ignore_cols=[],
include_cols=[],
treatment_effect_col="treatment_effect",
learner=None,
effect_learner=None,
min_df=0.05,
max_df=0.5,
ngram_range=(1, 1),
stop_words="english",
verbose=1,
):
"""
```
Infers causality from the data contained in `df` using a metalearner.
This function is a wrapper to the CausalNLP.CausalInferenceModel class.
For more details on methods and capabilities of the returned `CausalInferenceModel` object,
see the [CausalNLP documentation](https://amaiya.github.io/causalnlp/causalinference.html).
Usage:
>>> cm = causal_inference_model(df,
treatment_col='Is_Male?',
outcome_col='Post_Shared?', text_col='Post_Text',
ignore_cols=['id', 'email'])
cm.fit()
**Parameters:**
* **df** : pandas.DataFrame containing dataset
* **method** : metalearner model to use. One of {'t-learner', 's-learner', 'x-learner', 'r-learner'} (Default: 't-learner')
* **metalearner_type** : Alias of **method** parameter for backwards compatibility. If not None, overrides method.
* **treatment_col** : treatment variable; column should contain binary values: 1 for treated, 0 for untreated.
* **outcome_col** : outcome variable; column should contain the categorical or numeric outcome values
* **text_col** : (optional) text column containing the strings (e.g., articles, reviews, emails).
* **ignore_cols** : columns to ignore in the analysis
* **include_cols** : columns to include as covariates (e.g., possible confounders)
* **treatment_effect_col** : name of column to hold causal effect estimations. Does not need to exist. Created by CausalNLP.
* **learner** : an instance of a custom learner. If None, a default LightGBM will be used.
# Example
learner = LGBMClassifier(num_leaves=1000)
* **effect_learner**: used for x-learner/r-learner and must be regression model
* **min_df** : min_df parameter used for text processing using sklearn
* **max_df** : max_df parameter used for text procesing using sklearn
* **ngram_range**: ngrams used for text vectorization. default: (1,1)
* **stop_words** : stop words used for text processing (from sklearn)
* **verbose** : If 1, print informational messages. If 0, suppress.
**Returns:**
`CausalNLP.CausalInferenceModel` object
```
"""
try:
import causalnlp
except ImportError:
raise Exception("CausalNLP must be installed: pip install causalnlp")
from causalnlp import CausalInferenceModel
return CausalInferenceModel(
df,
method=method,
metalearner_type=metalearner_type,
treatment_col=treatment_col,
outcome_col=outcome_col,
text_col=text_col,
ignore_cols=ignore_cols,
include_cols=include_cols,
treatment_effect_col=treatment_effect_col,
learner=learner,
effect_learner=effect_learner,
min_df=min_df,
max_df=max_df,
ngram_range=ngram_range,
stop_words=stop_words,
verbose=verbose,
)</code></pre>
</details>
</dd>
</dl>
</section>
<section>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="ktrain.tabular" href="index.html">ktrain.tabular</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="ktrain.tabular.causalinference.causal_inference_model" href="#ktrain.tabular.causalinference.causal_inference_model">causal_inference_model</a></code></li>
</ul>
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