jasonamilne/causality

This repository aims to provide a comprehensive collection of methods for performing causal inference, including experimental designs, statistical methods, and advanced machine learning techniques. Each method will be implemented and documented with examples.

Causality Documentation

This repository aims to provide a comprehensive collection of methods for performing causal inference, including experimental designs, statistical methods, and advanced machine learning techniques. Each method will be implemented and documented with examples.

Table of Contents

1. Introduction
2. Methods
   • Experimental Methods
   • Quasi-Experimental Methods
   • Matching and Reweighting Methods
   • Graphical and Structural Methods
   • Machine Learning and Advanced Statistical Methods
   • Time-Series and Panel Data Methods
   • Mediation and Moderation Analysis
   • Sensitivity Analysis and Robustness Checks
   • Advanced Reweighting and Balancing Methods
   • Instrumental Variable Extensions
   • Subgroup Analysis and Heterogeneity
   • Other Methods
   • Emerging and Hybrid Methods
   • Causal Inference Software Tools
3. Development Plan
4. Contributing
5. License

Introduction

Causal inference aims to identify causal relationships between variables, going beyond simple correlations. This repository contains implementations of various causal inference methods, with examples and documentation for each method.

Methods

Experimental Methods

1. Randomized Controlled Trials (RCTs)
2. Field Experiments
3. Lab Experiments
4. Natural Experiments

Quasi-Experimental Methods

5. Instrumental Variables (IV)
6. Difference-in-Differences (DiD)
7. Regression Discontinuity Design (RDD)
8. Interrupted Time Series Analysis

Matching and Reweighting Methods

9. Propensity Score Matching (PSM)
10. Covariate Matching
11. Inverse Probability Weighting (IPW)
12. Genetic Matching
13. Entropy Balancing
14. Mahalanobis Distance Matching
15. Coarsened Exact Matching (CEM)
16. Nearest Neighbor Matching

Graphical and Structural Methods

17. Causal Diagrams (Directed Acyclic Graphs - DAGs)
18. Structural Equation Modeling (SEM)
19. Path Analysis

Machine Learning and Advanced Statistical Methods

20. Causal Forests
21. Bayesian Causal Inference
22. Double Machine Learning (DML)
23. Targeted Maximum Likelihood Estimation (TMLE)
24. Synthetic Control Method
25. G-computation
26. Marginal Structural Models (MSM)
27. Causal Bayesian Networks
28. Causal Discovery Algorithms (references: PC Algorithm, FCI Algorithm)

Time-Series and Panel Data Methods

29. Fixed Effects Models
30. Random Effects Models
31. Dynamic Panel Models
32. Panel Data Matching

Mediation and Moderation Analysis

33. Mediation Analysis
34. Moderation Analysis
35. Moderated Mediation Analysis

Sensitivity Analysis and Robustness Checks

36. Sensitivity Analysis
37. Bounds Analysis
38. Placebo Tests
39. Permutation Tests

Advanced Reweighting and Balancing Methods

40. Inverse Probability of Treatment Weighting (IPTW)
41. Standardized Mortality Ratio Weighting (SMRW)
42. Calibration Weighting

Instrumental Variable Extensions

43. Two-Stage Least Squares (2SLS)
44. Generalized Method of Moments (GMM)
45. Limited Information Maximum Likelihood (LIML)
46. Control Function Approach

Subgroup Analysis and Heterogeneity

47. Subgroup Analysis
48. Quantile Treatment Effects (QTE)
49. Heterogeneous Treatment Effects Analysis

Other Methods

50. Matching with Multiple Controls
51. Propensity Score Stratification
52. Propensity Score Regression Adjustment
53. Cross-Over Designs
54. Regression Kink Design
55. Fuzzy Regression Discontinuity Design
56. Sharp Regression Discontinuity Design

Emerging and Hybrid Methods

57. Network Causal Inference
58. Spatial Causal Inference
59. Integrative Causal Inference (combining multiple methods)

Causal Inference Software Tools

60. Epidemiological Software (references: DAGitty)
61. Statistical Packages (references: R's MatchIt, twang for IPW, Zelig, Python's causalml, DoWhy)
62. Machine Learning Libraries (references: econML, causalForest in R)

Development Plan

The development plan involves implementing each method step-by-step, providing detailed documentation and examples for each. Here is a proposed plan:

1. Initial Setup

   • Set up the repository structure.
   • Define coding standards and guidelines.

2. Phase 1: Basic Methods

   • Implement and document basic methods such as RCTs, IV, and DiD.
   • Provide examples and use cases for each method.

3. Phase 2: Intermediate Methods

   • Implement matching and reweighting methods.
   • Include detailed documentation and examples.

4. Phase 3: Advanced Methods

   • Implement graphical and structural methods, machine learning methods, and time-series methods.
   • Provide complex examples and case studies.

5. Phase 4: Robustness and Sensitivity Analysis

   • Implement sensitivity analysis and robustness checks.
   • Document common pitfalls and how to address them.

6. Phase 5: Emerging Methods

   • Implement and document emerging and hybrid methods.
   • Include innovative applications and case studies.

7. Final Phase: Integration and Testing

   • Integrate all methods into a cohesive framework.
   • Conduct comprehensive testing and validation.
   • Prepare final documentation and examples.

Contributing

We welcome contributions from the community. Please follow our contributing guidelines to get started.

License

This project is licensed under the MIT License - see the LICENSE file for details.