Introduction to Causal Inference with PPLs

This page cannot be shown here. You can still read it on the original site — the toolbar below keeps your place in the directory.

Causal inference asks a deceptively simple question: “What would have happened if things were
different?” Whether we’re evaluating a job training program, testing a new medical treatment,
or analyzing the impact of a policy change, we want to understand the causal effect of an
intervention not just observe correlations in the data. 
 Traditional statistical methods often struggle…

Read on /intro_causal_inference_ppl_pymc/ ↗