Editing Causal Inference in AI (section)

== <span style="color: #FFFFFF;">Evaluating</span> ==
Causal inference evaluation is uniquely challenging because we can never observe the true counterfactual:

'''Simulation studies (synthetic data)''': Generate data from a known causal model where the true ATE is known. Evaluate whether each estimator recovers the true ATE. This is the standard way to compare methods.

'''Semi-synthetic benchmarks''': Use real covariate data but simulate outcomes from a known causal model. ACIC (Atlantic Causal Inference Conference) benchmarks provide standardized challenges.

'''Sensitivity analysis''': Test how robust the estimate is to violations of key assumptions (e.g., unmeasured confounding). E-values quantify the minimum strength of unmeasured confounding that would overturn the result.

'''Refutation tests (DoWhy)''': Specific tests designed to detect estimation problems:
* Adding a random confounder shouldn't change a good estimate
* Replacing treatment with a random variable should produce ATE ≈ 0
* Placebo treatment (observed but causally irrelevant) should produce ATE ≈ 0

Expert practitioners present causal estimates with explicit assumption documentation, sensitivity analyses, and refutation test results — not just a point estimate. An ATE that fails refutation tests or is sensitive to unmeasured confounding should be reported with appropriate uncertainty.
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