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Overview of Causal Inference

In this video we provide an overview of causal inference. We begin by explaining under what conditions observed statistical relationships—such as conditional probabilities, conditional means, correlations, and linear regression coefficients— reflect true causal effects. We then introduce the concept of confounding factors and demonstrate how they can distort these observed relationships. Finally, we show how to adjust for known confounders to better estimate causal effects. Throughout the video, we illustrate each concept using four running examples. Probability and statistics book: https://a.co/d/7k259eb Website with free preprint, videos, slides and solutions to exercises: https://www.ps4ds.net Slides for this video: https://github.com/cfgranda/ps4ds/blo... Other videos on causal inference: https://www.ps4ds.net/videos/causalin...