Comparer des méthodes

	Pondération par l'inverse de la probabilité de traitement (IPW / IPTW)	Identification causale avec les graphes acycliques dirigés (do-calculus)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2000	2009
Auteur d'origine	Robins, Hernán & Brumback	Judea Pearl
Type	Causal inference weighting estimator	Causal identification framework
Source fondatrice	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.	Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606
Alias	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)
Apparentées	5	5
Résumé	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.	DAG causal identification is a framework, developed by Judea Pearl (2009), that encodes causal assumptions as a directed acyclic graph and uses the do-calculus rules to determine whether and how a causal effect can be identified from observational data. It systematically handles confounders, instrumental variables, and backdoor paths.