awesome-causality-algorithms

An index of algorithms for learning causality with data.

Please cite our survey paper if this index is helpful.

@article{guo2020survey,
  title={A survey of learning causality with data: Problems and methods},
  author={Guo, Ruocheng and Cheng, Lu and Li, Jundong and Hahn, P Richard and Liu, Huan},
  journal={ACM Computing Surveys (CSUR)},
  volume={53},
  number={4},
  pages={1--37},
  year={2020},
  publisher={ACM New York, NY, USA}
}

Table of Contents

• Toolboxes
• Learning Causal Effects
• Connections to Machine Learning

Toolboxes

Learning Causal Effects

With i.i.d Data

For Individual-level Treatment Effects (ITEs)

For Average-level Treatment Effects: ATE, ATT or ATC

For Continuous Treatment Effects

Vectorized Treatments

Treatment Responder Classification

Learning Causal Effects as Response Curves

Learning Causal Effects with Multi-cause Data

Transfer Learning for Learning Causal Effects

Variable Selection/Importance for Learning Causal Effects

Instrumental Variables

Non-i.i.d Data

Learning Causal Effects from Networked Observational Data

Learning Causal Effects under Spillover Effect/Interference

Learning Time Varying/Dependent Causal Effects

Connections to Machine Learning

Recommendation

Learning to Rank

Off-line Policy Evaluation/Optimization

Natural Language Processing

Counterfactual Explanations

Counterfactual Fairness

Reinforcement Learning

Multi-Armed Bandit/Causal Bandit

Invariant Prediction

Causality and GAN

Learning Causal Relations

With i.i.d. Data

Learning IV

Distinguishing Cause from Effect (Bivariate)

Conditional Independence Tests (for Constraint-based Algorithms)

Causal Discovery Meets Probabilistic Logic Programming

Learning Causal Relationships with non-i.i.d. Data

Scalable Ensemble Causality Discovery