A collection of visual guides designed to help applied scientists learn causal inference.
Current guides include:
- Targeted Maximum Likelihood Estimation (TMLE), a doubly robust semiparametric estimation method commonly used for causal inference. The guide shows the steps for estimating the mean difference in outcomes, adjusted for confounders, for a binary outcome and binary treatment. A full tutorial with
R
code is available on my blog.
- Superlearning (also known as stacking), an ensemble learning method recommended to use with TMLE. A full tutorial with
R
code is available on my blog.
- Definitions of causal inference intervention types with examples using U.S. air pollution data and a corresponding blog post
Recently I've also been playing around with comics for causal inference concepts! Here's a few so far:
To cite this repository, please use:
Katherine Hoffman. (2020, November 27). hoffmakl/causal-inference-visual-guides: Visual Guides for Causal Inference (Version 1.01). Zenodo. http://doi.org/10.5281/zenodo.4293931
Laan, M. J., & Rose, S. (2011). Targeted learning: Causal inference for observational and experimental data. New York: Springer.