Policymakers often need to make decisions when the implications of those decisions are not known with certainty. In many cases they rely in part on statistical evidence to guide these decisions. This requires statistical methods for estimating causal effects, that is the impact of these interventions. In this course we study how to analyze causal questions using statistical methods. We look at several causal questions in detail. For each case, we study various statistical and econometric methods that may shed light on these questions. We discuss what the critical assumptions are that underly these methods and how to assess whether the methods are appropriate for the settings at hand. We then analyze data sets, partly in class, and partly in assignments, to see how much we learn in practice. Pre-requisites: One quarter course in statistics, at the level of STATS DATASCI 116 or STATS DATASCI 117. Programming experience with Python will be helpful but is not required.
3 units · Letter or Credit/No Credit
Policymakers often need to make decisions when the implications of those decisions are not known with certainty. In many cases they rely in part on statistical evidence to guide these decisions. This requires statistical methods for estimating causal effects, that is the impact of these interventions. In this course we study how to analyze causal questions using statistical methods. We look at several causal questions in detail. For each case, we study various statistical and econometric methods that may shed light on these questions. We discuss what the critical assumptions are that underly these methods and how to assess whether the methods are appropriate for the settings at hand. We then analyze data sets, partly in class, and partly in assignments, to see how much we learn in practice. Pre-requisites: One quarter course in statistics, at the level of STATS 116 or STATS 117. Programming experience with Python will be helpful but is not required.
Offered in Autumn 2025 at Stanford University.