CausalML
CausalML refers to the field and, in some contexts, to open source software that applies machine learning methods to causal inference. It focuses on estimating the effects of interventions or treatments using data from experiments or observational studies and on understanding how these effects vary across individuals or subgroups.
In the standard causal inference framework, outcomes are described by potential outcomes Y(0) and Y(1), corresponding
Common techniques fall into several families. Propensity score methods (matching, weighting) balance treatment groups on covariates.
Applications span marketing optimization, healthcare, policy evaluation, and A/B testing in tech. Challenges include unmeasured confounding,