Causal Inference

Causal Inference

Reference for implementing causal inference methods: from identification strategy to estimation to diagnostics and robustness. Covers the major quasi-experimental and observational methods used in applied economics and quantitative social science.

When to Use This Skill

Use when the user is:

• Choosing an identification strategy for a causal question
• Implementing IV/2SLS, DiD, RDD, synthetic control, or matching
• Debugging specification issues (weak instruments, parallel trends violations, bandwidth sensitivity)
• Running robustness checks or falsification tests
• Working with modern DiD methods for staggered treatment timing

Skip when:

• The task is structural estimation (use structural-modeling skill)
• The task is pure prediction/ML (no causal question)
• The user needs simulation design (use numerical-auditor agent)

Where to Start

• Choosing a method? Jump to Method Selection Guide at the end
• Implementing a specific method? Go directly to that method's section below
• Need full code? See references/method-implementations.md for complete implementations

Frameworks

Two complementary frameworks underpin all causal inference:

Potential Outcomes (Rubin): Define Y(1), Y(0) as potential outcomes under treatment and control. The causal effect is τ = Y(1) - Y(0). The fundamental problem: we never observe both for the same unit. All methods are strategies for constructing valid counterfactuals.

DAGs (Pearl): Graphical models encoding conditional independence assumptions. Use d-separation to determine what must be conditioned on (and what must NOT be conditioned on) to identify causal effects. Particularly useful for reasoning about bad controls (colliders, mediators), overcontrol bias, and which instruments satisfy the exclusion restriction.

Quick Reference: Methods at a Glance