From Bernie Black (Northwestern) & Mathew McCubbins (USC):
The third (annual) Workshop on Research Design for Causal Inference, sponsored by Northwestern University, University of Southern California, the Society for Empirical Legal Studies (SELS), and the Searle Center on Law, Regulation, and Economic Growth, is scheduled for August 6-10, 2012, at Northwestern Law School, Chicago, IL. A brief description follows:
"Research design for causal inference is at the heart of a 'credibility revolution' in empirical research. We will cover the design of true randomized experiments and contrast them to simulations and quasi-experiments, where part of the sample is 'treated' in some way, and the remainder is a control group, but the researcher controls neither the assignment of cases to treatment and control groups nor administration of the treatment. We will assess the kinds of causal inferences one can and cannot draw from a research design, threats to valid inference, and research designs that can mitigate those threats.
Most empirical methods courses begin with the methods. They survey how each method works, and what assumptions each relies on. We will begin instead with the goal of causal inference, and discuss how to design research to come closer to that goal. The methods reflect the goal and are often adapted to the needs of a particular study. Some of the methods we will discuss are covered in PhD programs, but rarely in depth, and rarely with a focus on causal inference and on which methods to prefer for messy, real-world datasets with limited sample sizes.
Each day will conclude with a Stata 'workshop' where we will illustrate selected methods with real data and Stata code.
Target audience
Quantitative empirical researchers (faculty and graduate students) in social science, including law, political science, economics, many business-school areas (finance, accounting, management, marketing, etc), sociology, education, psychology, etc.–indeed anywhere that causal inference is important.
Minimum prior knowledge
We will assume knowledge, at the level of an upper-level college econometrics or applied statistics course, of how to run multivariate regressions, including OLS, logit, and probit; familiarity with basic probability and statistics including conditional and compound probabilities, confidence intervals, t-statistics, and standard errors; and some understanding of instrumental variables are and how they are used."
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