Editing Causal Inference in AI (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Causal Inference Methods Comparison
! Method !! Assumptions !! When to Use !! Python Library
|-
| Regression adjustment || No unmeasured confounding, correct functional form || Known confounders, sufficient data || statsmodels, DoWhy
|-
| Propensity score matching || No unmeasured confounding || Binary treatment, observational data || DoWhy, CausalML
|-
| Instrumental variables || Valid instrument exists || Hidden confounders, instrument available || DoWhy, linearmodels
|-
| Difference-in-differences || Parallel trends assumption || Panel data, natural experiment || CausalPy, statsmodels
|-
| Causal forest || No unmeasured confounding || Heterogeneous treatment effects || EconML, GRF (R)
|-
| Regression discontinuity || Local continuity at threshold || Sharp threshold in treatment assignment || RDD (R), DoWhy
|}

'''Key pitfalls and failure modes:'''
* '''Conditioning on colliders''' — Incorrectly conditioning on a variable that is a common effect (not cause) of treatment and outcome opens spurious paths rather than blocking them. Using a DAG is essential to identify what to condition on.
* '''Positivity violation''' — If some subgroups never receive (or always receive) treatment, causal effects for those subgroups cannot be estimated from data. Check overlap in propensity score distributions.
* '''Model misspecification''' — Parametric methods (regression adjustment) assume a specific functional form. Use doubly-robust or non-parametric methods (causal forests) to reduce this risk.
* '''Weak instruments''' — IV estimation with a weak instrument (low correlation with treatment) produces large, unreliable estimates. Test for instrument strength (F-statistic > 10 rule of thumb).
* '''Extrapolation beyond support''' — Causal effect estimates are only reliable within the range of the observed data. Be cautious about extrapolating to new populations or intervention levels.
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