This course focuses on the identification and estimation of causal effects. The course is divided into three blocks.

The first block introduces the concepts of potential outcomes, estimands and discusses inference of casual effect in a randomized controlled trial (RCT).

The second block discusses causal inference with observational data, that is data that are collected from registers or surveys. The focus is on, what is known as, quasi-experimental designs. The reason for the definition is that the modelling builds on the concepts from the RCT. We first discuss the Rubin Casual Model. The fundamental assumption for this model is the unconfoundedness assumption, which means that we can create an RCT using pre-treatment observed covariates. We then discusses designs and/or methods that do not rely on the unconfoundedness assumptions. Methods or design often used in micro-econometric applications.

Imbens, G. W. and Rubin, D. B. (2015). Causal Inference in Statistics, Social, and Biomedical Sciences. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press

Imbens G.W. and J.M. Wooldridge (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature 47,. 5-86

Wooldridge J. (2010). Econometric Analysis of Cross section and panel data MIT press.

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