Minisode 7.5 - Kristen's Causal Inference Workshop
Got something to say to this podcast? Send a voicemail, and you might hear yourself on the next episode! Wading past the shallow waters of simple correlation and straight into the statistical deep end. If your workflow relies on a naive linear regression, you aren't doing causal inference—you're just admiring your own residual error. In this 15-minute technical workshop, we break down the Neyman-Rubin counterfactual framework, unpack how standardization and G-computation simulate a randomized trial, and explore how inverse probability weighting (IPW) uses propensity scores to sever backdoor confounding paths. Essential listening for anyone trying to extract real causal signal from messy observational data.
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