This repository is licensed under a CC BY 4.0. This means you are free to share or adapt these materials however you see fit as long as you provide attribution (Kat Hoffman).

Current guides and educational illustrations include:

1. 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.

2. 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.

3. G-computation (also called the parametric g-formula), an estimation method for causal inference which involves estimating outcome regressions under substitutions to the data.

4. Inverse Probability Weighting an estimation method for causal inference which involves estimating treatment regressions (propensity scores) and reweighting the observed outcomes.

4. Causal inference intervention types with examples using U.S. air pollution data and a corresponding blog post

5. Identification vs. Estimation in casual inference.

I've also recently been playing around with comics for causal inference concepts. Here's a few so far:

Laan, M. J., & Rose, S. (2011). Targeted learning: Causal inference for observational and experimental data. New York: Springer.

Visual Guides for Causal Inference by Kat Hoffman is licensed under CC BY 4.0