7 Endogenous Treatments

Module 7 Slides

Textbook reading

Imbens, G.W., Rubin, D.B. (2015) Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction, Chapters 23-25 (Instrumental Variables).

Angrist, J.D., Pischke, J.S. (2009) Mostly Harmless Econometrics, Chapter 4 (Instrumental Variables in Action).

Pearl, J. (2009) Causality: Models, Reasoning, and Inference, 2nd Edition, Chapters 3-4.

Additional Readings

Instrumental Variables and LATE

Angrist, J.D., Imbens, G.W., Rubin, D.B. (1996) “Identification of Causal Effects Using Instrumental Variables,” Journal of the American Statistical Association, 91(434):444-455.

Imbens, G.W., Angrist, J.D. (1994) “Identification and Estimation of Local Average Treatment Effects,” Econometrica, 62(2):467-475.

Imbens, G.W. (2014) “Instrumental Variables: An Econometrician’s Perspective,” Statistical Science, 29(3):323-358.

Imbens, G.W., Rubin, D.B. (1997) “Bayesian Inference for Causal Effects in Randomized Experiments with Noncompliance,” Annals of Statistics, 25(1):305-327.

Weak Instruments and Partial Identification

Bound, J., Jaeger, D.A., Baker, R.M. (1995) “Problems with Instrumental Variables Estimation When the Correlation Between the Instruments and the Endogenous Explanatory Variable is Weak,” Journal of the American Statistical Association, 90(430):443-450.

Stock, J.H., Yogo, M. (2005) “Testing for Weak Instruments in Linear IV Regression,” in Identification and Inference for Econometric Models.

Kitagawa, T. (2015) “A Test for Instrument Validity,” Econometrica, 83(5):2043-2063.

Huber, M., Mellace, G. (2015) “Testing Instrument Validity for LATE Identification Based on Inequality Moment Constraints,” Review of Economics and Statistics, 97(2):398-411.

Principal Stratification

Frangakis, C.E., Rubin, D.B. (2002) “Principal Stratification in Causal Inference,” Biometrics, 58(1):21-29.

Zhang, J.L., Rubin, D.B. (2003) “Estimation of Causal Effects via Principal Stratification When Some Outcomes are Truncated by ‘Death’,” Journal of Educational and Behavioral Statistics, 28(4):353-368.

Jo, B., Stuart, E.A. (2009) “On the Use of Propensity Scores in Principal Causal Effect Estimation,” Statistics in Medicine, 28(23):2857-2875.

Mealli, F., Pacini, B. (2013) “Using Secondary Outcomes to Sharpen Inference in Randomized Experiments with Noncompliance,” Journal of the American Statistical Association, 108(503):1120-1131.

Mattei, A., Mealli, F., Pacini, B. (2014) “Identification of Causal Effects in the Presence of Nonignorable Missing Outcome Values,” Biometrics, 70(2):278-288.

Mediation Analysis

Robins, J.M., Greenland, S. (1992) “Identifiability and Exchangeability for Direct and Indirect Effects,” Epidemiology, 3(2):143-155.

Pearl, J. (2001) “Direct and Indirect Effects,” Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence, 411-420.

Imai, K., Keele, L., Yamamoto, T. (2010) “Identification, Inference and Sensitivity Analysis for Causal Mediation Effects,” Statistical Science, 25(1):51-71.

VanderWeele, T.J. (2015) Explanation in Causal Inference: Methods for Mediation and Interaction, Oxford University Press.

Structural Equation Models

Pearl, J. (2009) Causality: Models, Reasoning, and Inference, 2nd Edition, Cambridge University Press.

Bollen, K.A., Pearl, J. (2013) “Eight Myths About Causality and Structural Equation Models,” in Handbook of Causal Analysis for Social Research.

Software

R Packages

AER: Applied Econometrics with R Comprehensive package for 2SLS estimation, IV diagnostics, and weak instrument tests.

ivpack: Instrumental Variable Estimation Tools for IV analysis including Anderson-Rubin confidence intervals for weak instruments.

ivreg: Instrumental Variables Regression Modern implementation of 2SLS with robust standard errors and diagnostics.

mediation: Causal Mediation Analysis R package implementing methods from Imai, Keele, and Yamamoto (2010) for mediation analysis.

dagitty: Graphical Analysis of Structural Causal Models Tools for analyzing DAGs, finding adjustment sets, and testing instrumental variable assumptions.

ggdag: Visualize DAGs ggplot2 extension for visualizing and analyzing directed acyclic graphs.

grf: Generalized Random Forests Includes instrumental_forest() for IV estimation with heterogeneous effects.

Python Packages

linearmodels IV estimation (2SLS, LIML, GMM) with panel data support and robust inference.

DoWhy Microsoft’s causal inference library with DAG-based identification, IV estimation, and sensitivity analysis.

econml Includes DMLIV and OrthoIV for doubly robust IV estimation with machine learning.

CausalNex Bayesian network structure learning and causal inference with DAGs.