The course discusses problem of statistical inference in the social sciences and techniques for improving the validity of these inference. Problems of statistical inference mainly occur because theories remain partly inconclusive in respect to model specification and thus model uncertainty emerges. Discussed techniques include (but are not limited to): randomized trials, regression discontinuity, matching, instrumental equation and structural equation models, and robustness tests.

The course will help Ph.D. students to develop research designs that allow deriving statistical and causal inferences from empirical analysis. Students will learn about the different methodologies, techniques and research designs for statistical and causal inferences.  

The course uses standard rules for absence.

The course relies on a combination of seminar style teaching and participants' presentations of research designs.

Classroom participation (20 percent), presentation of research design (50 percent) and and homework (30 percent).