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Double machine learning (DML) has become an increasingly popular tool for automated variable selection in high-dimensional settings.
The statistical implications of a system of simultaneous equations
Haavelmo, T. (1943) · 1943
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Cowles Foundation Monograph 10: Statistical Inference in Dynamic Economic Models
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Quantifying biases in causal models: Classical confounding vs collider-stratification bias
Greenland, S. (2003) · 2003
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Making Things Happen
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Nonparametric estimation of average treatment effects under exogeneity: A review
Imbens, G. W. (2004) · 2004
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Hunting Causes and Using Them
Cartwright, N. (2007) · 2007
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Mostly Harmless Econometrics: An Empiricist’s Companion
Angrist, J. D. and J.-S. Pischke (2009) · 2009
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Pearl, J. (2009) · 2009
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Identification, inference and sensitivity analysis for causal mediation effects
Imai, K., L. Keele, and T. Yamamoto (2010) · 2010
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Causal inference and the data-fusion problem
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The state of applied econometrics: Causality and policy evaluation
Athey, S. and G. W. Imbens (2017) · 2017
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Double/debiased machine learning for difference-in-differences models
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Taming the factor zoo: A test of new factors
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Reducing model misspecification and bias in the estimation of interactions
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Estimating identifiable causal effects through double machine learning
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