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Causal inference in public policy

Program evaluation, DiD around legislation, and evidence standards for policymakers.

Governments need to know whether programs cause employment gains, health improvements, or unintended side effects. Budget cycles demand timely evidence; RCTs are rare at scale.

DiD, IV, and synthetic control methods are standard in policy econometrics. Transparent assumptions help officials defend decisions to the public.

Run this method on your data — no Python

CausalLens runs matching, DiD, causal forests, DoWhy refutation, and more — with balance tables, sensitivity checks, and PDF export.

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