In statistics, econometrics, epidemiology, genetics and related disciplines,<unk> s (also known as path diagrams,<unk>  Bayesian networks or DAGs) are probabilistic<unk> ical models used to encode assumptions about the data-generating process.<unk> s can be used for communication and for inference. As communication devices, the<unk> s provide formal and transparent representation of the<unk>  assumptions that researchers may wish to convey and defend. As inference tools, the<unk> s enable researchers to estimate effect sizes from non-experimental data, derive testable implications of the assumptions encoded, test for external validity, and manage missing data and selection bias.<unk> s were first used by the geneticist Sewall Wright under the rubric "path diagrams". They were later adopted by social scientists and, to a lesser extent, by economists. These models were initially confined to linear equations with fixed parameters. Modern developments have extended<unk> ical models to non-parametric analysis, and thus achieved a generality and flexibility that has transformed<unk>  analysis in computer science, epidemiology, and social science.
Causal graph
Mixing study
Consensus error grid
Platform trial
Montreal experiments
Causal model
Normalization model
Epidemiological method