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How Much Should We Trust Differences-In-Differences Estimates, M. Bertrand, et al, 2002
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The Impact of the Mariel Boatlift on the Miami Labor Market, David Card, 1990
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Worker Compensation and Injury Duration: Evidence From Natural Experiment, B. Meyer, et al, 1995
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The Estimation of Causal Effects by Difference-In-Difference Methods, Michael Lechner, 2011
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Introduction to Robust Estimation and Hypothesis Testing, Rand Wilcox, 4th edition, 2017
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On the Problem of the Most Efficient Tests of Statistical Hypotheses, J. Neyman, ES Pearson, 1933
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Statistical Methods and Scientific Induction, R. Fisher, 1955
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P values are not Error Probabilities, R. Hubbard, M.J. Bayarri, 2003
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Machine Learning - Probabilistic Perspective, Kevin Murphy, 2012
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Mostly Harmless Econometrics: An Empiricist's Companion, Joshua Angrist, Joern-Steffen Pischke, 2008
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Statistical Models: Theory and Practice, David A. Freedman, 2009
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Introduction to Causal Inference from Machine Learning Perspective (Lecture Notes), Brady Neal, 2020
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Indentify Causality by Difference in Differences with Chris Kuo
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Double Machine Learning for causal inference with Borja Velasco
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Econometrics in Python: Cross-Sectional Analysis with OLS with Augusto Gonzalez-Bonorino
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Quasi Experimental Techniques and Applicability : A Framework Approach with Ayushman Mathur
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Causal Inference - Structural Causal Models with Bruno Goncalves
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Causal Inference - Model Testing and Causal Search with Bruno Goncalves
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Causal Inference - The Adjustment Formula with Bruno Goncalves
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Causal Inference - Front Door Criterion with Bruno Goncalves
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Causal Inference - Conditional Interventions and Covariate Specific Effects with Bruno Goncalves
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Causal Inference - Inverse Probability Weighing with Bruno Goncalves
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The Dual Roots of Belief Propagation and Causal Inference with Oliver Beige
related article: Machine Learning Methods for Estimating Heterogenous Causal Effects, Susan Athey, Guido Imbens, 2015
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A Tableau Calculus for the Analysis of Experiments with Jared Maruskin