Resources.md

Resources on Hypothesis Testing, Estimation of Treatment Effects and Generalized Synthetic Control

articles

• Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models, Y. Xu, 2017

• Generalized Synthetic Control for TestOps at ABI: Models, Algorithms, and Infrastructure, Luis Costa, et al, 2022

• How Much Should We Trust Differences-In-Differences Estimates, M. Bertrand, et al, 2002

• Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania, D. Card and A. Krueger, 1993

• The Impact of the Mariel Boatlift on the Miami Labor Market, David Card, 1990

• Worker Compensation and Injury Duration: Evidence From Natural Experiment, B. Meyer, et al, 1995

• The Estimation of Causal Effects by Difference-In-Difference Methods, Michael Lechner, 2011

• Double/Debiased Machine Learning for Treatment and Structural Parameters, Chernozhukov, V., et al, 2017

• The Paper On How: Estimating Treatment Effects Using the Front-Door Criterion, M. Bellemare, J. Bloem, 2020

Hypothesis Testing

• Testing Statistical Hypotheses, E.L. Lehmann, J.P. Romano, Third Edition, Stanford U., Springer, 2005

• Introduction to Robust Estimation and Hypothesis Testing, Rand Wilcox, 4th edition, 2017

• On the Problem of the Most Efficient Tests of Statistical Hypotheses, J. Neyman, ES Pearson, 1933

• The Design of Experiments, R.A. Fisher, 1935

• Statistical Methods and Scientific Induction, R. Fisher, 1955

• The Nature of Probability, R.A. Fisher, 1958

• The Fisher, Neyman-Pearson Theories of Testing Hypotheses: One Theory or Two? by E.L. Lehman, TR No. 333, 1992

• Models and Statistical Inference: The Controversy between Fisher and Neyman-Pearson, Johannes Lenhard, 2006

• P values are not Error Probabilities, R. Hubbard, M.J. Bayarri, 2003

books

• Machine Learning - Probabilistic Perspective, Kevin Murphy, 2012

• Statistical Models, A.C. Davison, 2008

• Mostly Harmless Econometrics: An Empiricist's Companion, Joshua Angrist, Joern-Steffen Pischke, 2008

• Statistical Models: Theory and Practice, David A. Freedman, 2009

• The Design of Experiments, R.A. Fisher, 1935

• Introduction to Causal Inference from Machine Learning Perspective (Lecture Notes), Brady Neal, 2020

Medium

• Indentify Causality by Difference in Differences with Chris Kuo

• Double Machine Learning for causal inference with Borja Velasco

• Causal Python - Elon Musk's Tweet, Our Googling Habits, and Bayesian Synthetic Control with Aleksander Molak

• Econometrics in Python: Cross-Sectional Analysis with OLS with Augusto Gonzalez-Bonorino

• Quasi Experimental Techniques and Applicability : A Framework Approach with Ayushman Mathur

• Introduction to Causal Inference with Mert Atli

• Causal Inference - Simpson's Paradox with Bruno Goncalves

• Causal Inference - Probability Theory with Bruno Goncalves

• Causal Inference - Graphs with Bruno Goncalves

• Causal Inference - Structural Causal Models with Bruno Goncalves

• Causal Inference - Chains and Forks with Bruno Goncalves

• Causal Inference - Colliders with Bruno Goncalves

• Causal Inference - d-separation with Bruno Goncalves

• Causal Inference - Model Testing and Causal Search with Bruno Goncalves

• Causal Inference - Interventions with Bruno Goncalves

• Causal Inference - The Adjustment Formula with Bruno Goncalves

• Causal Inference - Back Door Criterion with Bruno Goncalves

• Causal Inference - Front Door Criterion with Bruno Goncalves

• Causal Inference - Conditional Interventions and Covariate Specific Effects with Bruno Goncalves

• Causal Inference - Inverse Probability Weighing with Bruno Goncalves

• Causal Inference - Mediation with Bruno Goncalves

• The Need for Causal Inference with Robert Kuebler

• Causal Inference: Quasi-experiments with Ian Ho

• 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

• A Tableau Calculus for the Analysis of Experiments with Jared Maruskin

Related resources and links

• 15 Best Tools for ML Experiment Tracking and Management

• PyExperimenter: Easily distribute experiments and track results, Tornede, 2022