causal-inference

Causal treatment effect estimation from observational data requires combining a proposed causal graph with symbolic identification and estimation. This approach routes formal causal methods to calculate ATE, ATT, or CATE while controlling for confounding variables.

Counterfactual analysis answers "what-if" questions by applying intervention logic to a validated causal graph. You propose a directed acyclic graph from domain context, then use symbolic methods to identify and estimate outcomes for alternative scenarios.

Validating a suggested causal graph involves using constraint-based structure learning alongside domain context. Once proposed, the graph must pass validation before executing identification, estimation, and refutation steps to ensure reliable causal effect results.

Observational data supports causal root-cause analysis when confounding is present if you apply an explicit causal model. By identifying which variables causally explain observed outcomes, you can isolate true drivers from spurious correlations.

Refutation tests are needed for causal effect estimation because they provide falsification results rather than just point estimates. This refutation-first reliability approach ensures the output includes quantified uncertainty and interpretable validation of the causal model.