Editing Causal Inference in AI (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing a causal inference analysis pipeline for business decision-making:

'''1. Problem formulation'''
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Define: What is the treatment? What is the outcome? What is the population?
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Draw the causal DAG: variables, causal paths, potential confounders
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Apply backdoor criterion: which variables block all confounding paths?
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Check identifiability: can the causal effect be estimated from available data?
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Assess data availability: are the required conditioning variables measured?
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'''2. Estimation pipeline'''
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Data collection: ensure confounders are measured, check positivity
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Covariate balance check: compare treatment/control distributions
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Propensity score modeling (if observational): logistic regression or GBM
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Effect estimation: doubly-robust AIPW or causal forest for heterogeneous effects
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Sensitivity analysis: E-value, Rosenbaum bounds
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Refutation tests: DoWhy refute suite
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Report: ATE ± CI, assumptions, sensitivity, subgroup effects
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'''3. Causal ML in production (uplift modeling)'''
* Estimate heterogeneous treatment effects: which users benefit most from intervention?
* Use X-learner or causal forest to estimate CATE (Conditional ATE) per user
* Target intervention to users with highest predicted CATE
* Monitor actual vs. predicted uplift in A/B tests post-deployment
* Continuously retrain causal model as new experimental data arrives

[[Category:Artificial Intelligence]]
[[Category:Machine Learning]]
[[Category:Causal Inference]]
[[Category:Statistics]]
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