विधियों की तुलना करें

	विषम उपचार प्रभाव (CATE / मेटा-लर्नर्स)	साधनात्मक चर द्विपदीय न्यूनतम वर्ग (IV/2SLS) के माध्यम से
क्षेत्र	कारणात्मक अनुमान	कारणात्मक अनुमान
परिवार	Regression model	Regression model
उद्भव वर्ष	2018	2009
प्रवर्तक	Wager & Athey (causal forest); Künzel et al. (meta-learners)	Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory)
प्रकार	Causal machine-learning framework	Instrumental-variables regression
मौलिक स्रोत	Wager, S. & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association.	Angrist, J. D. & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
उपनाम	conditional average treatment effect, CATE, meta-learners, causal forest	instrumental variables, IV estimation, 2SLS, instrumental variable regression
संबंधित	5	5
सारांश	Heterogeneous Treatment Effects is a machine-learning framework that estimates how a treatment effect varies across individuals — the conditional average treatment effect (CATE). It bundles meta-learner strategies such as the T-Learner, S-Learner, X-Learner and R-Learner alongside the causal forest of Wager and Athey (2018) and Künzel et al. (2019).	IV/2SLS is a two-stage estimation method that recovers the causal effect of an endogenous regressor by isolating the part of its variation driven by an external instrument. It is the workhorse identification strategy in modern applied econometrics, developed at length in Angrist and Pischke's Mostly Harmless Econometrics (2009).