Observed Signal · Jul 23, 2026 · Technical Article · Source: Machine Learning Pills · Impact: 2/5 · Sentiment: Positive
From Randomized to Natural Experiments
This technical newsletter issue explains how to estimate causal effects when randomized experiments are unavailable. It reviews randomized controlled trials (RCTs) as the gold standard, then covers adjustment methods that rely on the unconfoundedness assumption (regression adjustment, matching, propensity scores, inverse-probability weighting, and doubly robust/AIPW estimators). It then describes quasi-experimental designs that address unmeasured confounding by exploiting natural experiments — difference-in-differences (DiD), synthetic control, regression discontinuity (RDD), and instrumental variables (IV). The piece presents a hierarchy of fallbacks (randomize if possible; otherwise adjust; otherwise use quasi-experimental designs) and notes that Part 3 will cover ML-based causal inference (Double Machine Learning, causal forests, uplift modeling). The issue contains a paid-subscriber note locking some additional methods.
Provides practical guidance on causal inference and experimental design relevant to measurement and analytics teams in advertising; educational but not platform-level policy or tech release.
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Key Takeaways & Evidence Grounding
- Randomized controlled trials (RCTs) are presented as the reference standard for causal identification across fields.
- Adjustment methods require the unconfoundedness assumption and include regression adjustment, matching, propensity-score methods, inverse-probability weighting (IPW), and doubly robust/AIPW estimators.
- Quasi-experimental designs discussed include difference-in-differences (DiD), synthetic control, regression discontinuity design (RDD), and instrumental variables (IV).
- Doubly robust estimation (AIPW) is highlighted and Double Machine Learning is noted as the subject of Part 3.
- Publication date (webpage metadata): 2026-07-23.
Ontology Mapping & Concepts
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From Average Effects to Personalized Causal Decisions
This technical newsletter explains how causal inference methods move beyond average treatment effects to estimate heterogeneous effects (CATE) and use them for personalized decisions. It surveys ML approaches — meta-learners (S-, T-, X-, R-learners), Double Machine Learning / residualisation, and causal forests — and explains uplift modelling for marketing and retention, including operational evaluation with Qini curves. Key practical guidance includes protecting randomized holdouts, expecting drift, and pairing CATE models with exploration. The piece notes standard implementations (grf in R, EconML in Python) and highlights that randomized data and careful cross-fitting are crucial for valid estimates and uncertainty quantification.
Causal Inference with Only Observational Data
This technical article explains how causal inference differs fundamentally from predictive modeling when only observational data are available. It argues that identification of causal effects requires explicit, pre-specified assumptions (not discoverable from the data) and recommends tools and designs—directed acyclic graphs (DAGs), adjustment for confounders, avoidance of colliders, and appropriate outcome models (e.g., survival analysis)—to make those assumptions explicit. The piece gives a concrete B2B example (onboarding calls and 12-month retention) illustrating confounding and Simpson’s paradox, lists four observational designs (regression/matching, propensity score/IPW, difference-in-differences, instrumental variables/regression discontinuity), and recommends robustness checks: sensitivity analysis, negative controls, specification variation, and checking overlap. It emphasizes honest, assumption-explicit reporting and that randomised experiments remain the gold standard.
L'Oréal's Baryshkov: Start with the decision, not dashboard
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