chen0031 / causal

notebooks on methods for causal inference

causal

Notebooks on methods for causal inference (Work in progress)

There are two types of notebooks (above, click on the title to view it)

• Summary of theory (brief)
• Detailed empirical examples applying the theory

Lauch notebooks:

Below are links to some unpolished YouTube videos with very brief summaries of the topics.

Brief intro/review lectures

• Intro to causal inference
• Potential outcome model
• Propensity score matching
• Instrumental variables
• Difference-in-Difference (Applied)
• Three strategies to estimate causal effects

See also

• Ben Lambert: A full course in econometrics

Useful links

• Microsoft: DoWhy

• Microsoft: EconML

• Uber: CausalML

• Laurence Wong: Causal inference in Python

• Dafiti: Causal impact

• Yusuke Minami: CausalLift

• Iain Barr: CausalGraphicalModels, Causal Graphical Models in Python

• akhelle: Causality

• Daggity: Daggity, Draw causal graphs

• Facebook: Prophet

• Adam Kelleher and Amit Sharma: Introducing the do-sampler for causal inference

• Iain: Causal inference with Python

• Vivian Zheng: Causal inference 101: difference-in-differences

• Yusuke Minami: CausalLift: Python package for Uplift Modeling in real-world business; applicable for both A/B testing and observational data My understanding of Causality comes mainly from the reading of the follow work:

• Overview of book by Pearl)

• If correlation doesn’t imply causation, then what does?](http://www.michaelnielsen.org/ddi/if-correlation-doesnt-imply-causation-then-what-does/), Michael Nielsen

• Jonas Peters' lecture notes, Jonas Peters

• Elements of Causal Inference

• Lecture notes on causality https://github.com/microsoft/EconML