Methods and Computation in Program Evaluation

3.0 credits · Undergraduate

Introduces fundamental frameworks for program evaluation and causal inference in empirical research and the industry, and studies how modern predictive machine learning methods can be applied to get more credible estimates of causal effects. Problems are formulated and discussed in terms of formal econometric models, but the focus will be the applied and practical perspectives, especially in economics. Requires statistic and econometric knowledge at the level of ECON 3140 or equivalent, and programing experience in R or Python.