This article compares six popular Python causal inference libraries—Bnlearn, Pgmpy, CausalNex, DoWhy, PyAgrum, and CausalImpact—using the U.S. Census Income dataset to answer whether a graduate degree raises the probability of earning over $50K, and provides detailed code, pros, cons, and results for each tool.
An in‑depth, five‑minute guide compares six popular Python causal inference libraries—Bnlearn, Pgmpy, CausalNex, DoWhy, PyAgrum, and CausalImpact—using the Census Income dataset to illustrate structure learning, parameter estimation, inference, and causal effect validation, highlighting each tool’s strengths, limitations, and ideal use cases.
This article explains why causal inference is essential for moving beyond correlation to decision‑making, introduces the structural causal model framework, and provides a step‑by‑step guide to using Microsoft’s DoWhy Python library for modeling, identification, estimation, and counterfactual analysis.
This article explains why causal inference is essential for decision‑making, contrasts it with pure prediction, outlines the four DoWhy steps (modeling, identification, estimation, refutation), and demonstrates a case study on road freight accidents using DoWhy and EconML with code examples and results.
