README.md

Causal inference is the process of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect. The main difference between causal inference and inference of association is that the former analyzes the response of the effect variable when the cause is changed. The science of why things occur is called etiology. Causal inference is an example of causal reasoning.

Papers

• Structural Intervention Distance (SID) for Evaluating Causal Graphs
• A fast PC algorithm for high dimensional causal discovery with multi-core PCs
• MERLiN: Mixture Effect Recovery in Linear Networks
• Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data
• Learning Representations for Counterfactual Inference
• Estimating individual treatment effect: generalization bounds and algorithms
• Ancestral Causal Inference
• Double/Debiased Machine Learning for Treatment and Structural Parameters
• Entropic Causal Inference
• RankPL: A Qualitative Probabilistic Programming Language
• Causal Effect Inference with Deep Latent-Variable Models
• Bias and high-dimensional adjustment in observational studies of peer effects
• Deep Counterfactual Networks with Propensity-Dropout
• On Adaptive Propensity Score Truncation in Causal Inference
• Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions
• RNN-based counterfactual prediction
• Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Quality
• Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution
• Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis
• Adversarial Generalized Method of Moments
• A Constraint-Based Algorithm For Causal Discovery with Cycles, Latent Variables and Selection Bias
• The Blessings of Multiple Causes
• Consistent Estimation of Propensity Score Functions with Oversampled Exposed Subjects
• Orthogonal Random Forest for Causal Inference
• Interpretable Almost Matching Exactly for Causal Inference
• Identifying Causal Effects with the R Package causal effect
• Local Linear Forests
• Challenges of Using Text Classifiers for Causal Inference
• Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach
• When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks
• Using Embeddings to Correct for Unobserved Confounding in Networks
• Machine learning in policy evaluation: new tools for causal inference
• Adapting Text Embeddings for Causal Inference
• An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal Inference
• Adapting Neural Networks for the Estimation of Treatment Effects
• Gradient-Based Neural DAG Learning
• Learning Individual Causal Effects from Networked Observational Data
• Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
• A neural network oracle for quantum nonlocality problems in networks
• Optimal Estimation of Generalized Average Treatment Effects using Kernel Optimal Matching
• Causal inference using Bayesian non-parametric quasi-experimental design
• CAUSALITY FOR MACHINE LEARNING
• A Causal Inference Method for Reducing Gender Bias inWord Embedding Relations
• Variable-lag Granger Causality for Time Series Analysis
• Variable-lag Granger Causality and Transfer Entropy for Time Series Analysis
• CausalML: Python Package for Causal Machine Learning
• Unbiased Scene Graph Generation from Biased Training
• Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding
• A Practical Introduction to Bayesian Estimation of Causal Effects: Parametric and Nonparametric Approaches
• Does Terrorism Trigger Online Hate Speech? On the Association of Events and Time Series
• Causal Modeling of Twitter Activity During COVID-19
• Identifying Causal Structure in Dynamical Systems
• Causal intersectionality for fair ranking
• ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion
• Autoregressive flow-based causal discovery and inference
• Structural Causal Models Are (Solvable by) Credal Networks
• Representation Learning for Treatment Effect Estimation from Observational Data
• Adapting Neural Networks for the Estimation of Treatment Effects
• Debiased Bayesian inference for average treatment effects
• Using Embeddings to Correct for Unobserved Confounding in Networks
• Outcome-adaptive lasso: variable selection for causal inference