Measuring Impact When You Can’t A/B Test — a Quick and Practical Guide to Causal Inference

Last Updated on September 22, 2026 by Editorial Team Author(s): Jonty Haberfield Originally published on Towards AI. A tour of methods including Propensity Score Matching, Double ML, Instrumental Variables, and Two Way Fixed Effects What do you do if you want to separate correlation from causation — if you want to know the actual, causal, impact of…something? A new drug, a re-designed app, a loyalty scheme. Photo by Erik Mclean on UnsplashAfter introducing why causal impact is hard without random assignment, the article builds a running “loyalty program” example and shows how naive comparisons overestimate uplift due to selection bias from confounders. It then walks through methods under the key assumption of no unobserved confounders: OLS regression (works only with a correctly specified linear world), Propensity Score Matching (pairing treated and control “twins” via propensity overlap, yielding an ATT-style estimate), IPTW (reweighting to emulate an RCT but sensitive to accurate propensity scores and producing an ATE), and Double ML (residualizing out confounders using cross-fitting and estimating a treatment effect close to the true value). The author clarifies what each estim…

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