Compara mètodes

	Pes pesat per la probabilitat inversa (IPW / IPTW)	La identificació causal amb grafs acíclics dirigits (do-càlcul)
Camp	Inferència causal	Inferència causal
Família	Regression model	Regression model
Any d'origen	2000	2009
Autor original	Robins, Hernán & Brumback	Judea Pearl
Tipus	Causal inference weighting estimator	Causal identification framework
Font seminal	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
Àlies	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)
Relacionats	5	5
Resum	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.