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Short Course on Experimental and Quasi-Experimental Inference Methods August 24–27, 2015 – Buenos Aires, Argentina Preliminary Outline – January 28, 2015

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Short Course on Experimental and Quasi-Experimental Inference Methods August 24–27, 2015 – Buenos Aires, Argentina Preliminary Outline – January 28, 2015 Short Course on Experimental and Quasi-Experimental Inference Methods August 24{27, 2015 { Buenos Aires, Argentina Preliminary Outline { January 28, 2015 1 General Information • Organized by The Abdul Latif Jameel Poverty Action Lab (J-PAL), Latin America & Caribbean regional office. • Location: Universidad Torcuato Di Tella, Buenos Aires, Argentina. • Duration: Monday 24{August through Thursday 27{August, 2015. • Schedule: 9.00am{12.00pm & 1.30pm{5.00pm. See below for details. 2 Instructors • Matias D. Cattaneo, Associate Professor of Economics, University of Michigan. [email protected] · www.umich.edu/∼cattaneo • Sebastian F. Galiani, Professor of Economics, University of Maryland, and Scientific Director, J-PAL Latin America & Caribbean. [email protected] · econweb.umd.edu/∼galiani • Roc´ıoTitiunik, Assistant Professor of Political Science, University of Michigan. [email protected] · www.umich.edu/∼titiunik Note: Research and related work underlying this short course was generously supported by the National Science Foundation (USA) through grant SES-1357561. 3 Overview and Required Background This short course provides an introduction to basic principles and recent methodological de- velopments in the analysis of experimental and quasi-experimental research designs in the Social Sciences. The course will focus on three main topics: (i) introduction to causal inference, (ii) analysis of randomized experiments, and (iii) regression discontinuity (RD) designs. A brief out- line of the course, along with some background references (including extensions, applications and theoretical results), is given below. Participants are expected to have elementary working knowledge of statistics, econometrics and program evaluation. It would be useful, but not required, if participants were familiar with basic results from the literature on program evaluation and treatment effects, at the level of Angrist and Pischke(2009). See also Wooldridge(2010) for a graduate level textbook review on econometrics and program evaluation. However, the course is designed to be self-contained and hence most underlying statistical and econometric concepts are explained in class. For more comprehensive technical reviews on causal inference, program evaluation and related ideas are given, see Heckman and Vytlacil(2007) and Imbens and Wooldridge(2009). 1 4 Outline & Background References The first three days will focus on causal inference and analysis of experiments. Main background references for the topics covered include Angrist and Pischke(2009), Imbens and Rubin(2015), Glennerster and Takavarashan(2013), Morgan and Winship(2015), and Gerber and Green(2012). The last day will focus on a quasi-experimental research designs. This year the course will discuss Regression Discontinuity (RD) designs. Main background references include Imbens and Lemieux(2008) and Lee and Lemieux(2010). The course will focus on some recent developments, following the results in Calonico, Cattaneo, and Titiunik(2014b,a, 2015a,b); Cattaneo, Frandsen, and Titiunik(2015). Day 1 (Mon 24-Aug 2015): Introduction 09.00am { 10.15am: Review of Statistical Inference. 10.45am { 12.00pm: Causal Inference and Experimental Designs. 01.30pm { 02.45pm: Simple versus Cluster Randomization. 03.15pm { 04.30pm: Power Analysis. 04.30pm { 05.00pm: Q&A and general discussion Day 2 (Tue 25-Aug 2015): Analysis of Experiments 09.00am { 10.15am: Finite-sample inference methods. 10.45am { 12.00pm: Large-sample and non-parametric inference methods. 01.30pm { 02.45pm: Randomization inference methods. 03.15pm { 04.30pm: Empirical Applications in Economics and Political Science. 04.30pm { 05.00pm: Q&A and general discussion Day 3 (Tue 26-Aug 2015): Departures from canonical RCTs 09.00am { 10.15am: Non-compliance. 10.45am { 12.00pm: Attrition and Bound Analysis. 01.30pm { 02.45pm: Multiple Testing. 03.10pm { 04.30pm: Empirical Applications in Economics and Political Science. 04.30pm { 05.00pm: Q&A and general discussion Day 4 (Thu 27-Aug 2015): RD Designs 09.00am { 10.20am: Introduction to RD designs. 10.45am { 12.00pm: RD designs as \local randomized experiments". 01.30pm { 02.45pm: Local-polynomial methods. 03.15pm { 04.30pm: Empirical Applications in Economics and Political Science. 04.30pm { 05.00pm: Q&A and general discussion 2 References Angrist, J. D., and J.-S. Pischke (2009): Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, Princeton, NJ. Calonico, S., M. D. Cattaneo, and R. Titiunik (2014a): \Robust Data-Driven Inference in the Regression-Discontinuity Design," Stata Journal, 14(4), 909{946. (2014b): \Robust Nonparametric Confidence Intervals for Regression-Discontinuity De- signs," Econometrica, 82(6), 2295{2326. (2015a): \Optimal Data-Driven Regression Discontinuity Plots," Journal of American Statistical Association, forthcoming. (2015b): \rdrobust: An R Package for Robust Inference in Regression-Discontinuity Designs," working paper, University of Michigan. Cattaneo, M. D., B. Frandsen, and R. Titiunik (2015): \Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate," Journal of Causal Inference, forthcoming. Gerber, A. S., and D. P. Green (2012): Field Experiments: Design, Analysis, and Interpreta- tion. W. W. Norton & Company. Glennerster, R., and K. Takavarashan (2013): Running Randomized Evaluations: A Prac- tical Guide. Princeton University Press. Heckman, J. J., and E. J. Vytlacil (2007): \Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation," in Handbook of Econometrics, vol. VI, ed. by J. Heckman, and E. Leamer, pp. 4780{4874. Elsevier. Imbens, G., and T. Lemieux (2008): \Regression Discontinuity Designs: A Guide to Practice," Journal of Econometrics, 142(2), 615{635. Imbens, G. W., and D. B. Rubin (2015): Causal Inference in Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press, New York. Imbens, G. W., and J. M. Wooldridge (2009): \Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, 47(1), 5{86. Lee, D. S., and T. Lemieux (2010): \Regression Discontinuity Designs in Economics," Journal of Economic Literature, 48(2), 281{355. Morgan, S. L., and C. Winship (2015): Counterfactuals and causal inference: Methods and principles for social research. Cambridge University Press, second edn. Wooldridge, J. (2010): Econometric Analysis of Cross-Section and Panel Data. MIT Press, Cambridge, MA. 3.
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