Bernie Black (Northwestern) and Mathew McCubbins (USC) are organizing what promises to be an exciting and much-needed workshop/conference focusing on research design aspects that bear on inference. The workshop, set for Friday, Aug. 16-20, 2010, will take place at Northwestern's law school in Downtown Chicago. One innovation is that the week-long (5-day) workshop program is modular in nature and will facilitate 3- and 4-day attendance. The Workshop's website, along with detailed information, is here. Excerpted information follows:
Overview: Research design for causal inference is at the heart of a “credibility revolution” in empirical research in the last 15 years that spans many fields. We will cover the design of true randomized experiments and contrast them to simulations and to 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 carefully describe the kinds of causal inferences one can and cannot draw from a research design, various threats to valid inference, and research designs commonly used to 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.
Target audience: Quantitative empirical researchers (faculty and graduate students) in social science, including law, political science, economics, most business-school areas (finance, accounting, marketing, etc), sociology, education, psychology – indeed anywhere that causal inference is important.