Recent Posts

In this post we discuss what is perhaps the most elegant technique for doing causal inference: regression discontinuity (RD). Although it is known today as a toolkit in the econometric toolbox, it was originally developed by an education researcher in 1960.

So far, we first saw that fixed effects was able to get rid of confounders that did not change over time. We then noted that difference in differences designs could do better, as they got rid of confounders that changed over time parallel to the whole control group.

In the previous post we explored the fixed effects approach to causal inference. Here we discuss the difference in differences approach, which is less widely applicable, but can make a stronger claim as to uncovering a cause.

Over the past few decades, economists have been the main drivers of causal analysis in social science. The most influential text has probably been Angrist and Pischke’s Mostly Harmless Econometrics.

In the previous blogpost, we used a basic decision tree and logistic regression to predict who would graduate high school among a bunch of kindergarten kids. In this post, we bring the random forest and XgBoost algorithms to bear on the data set, to see if they can improve the predictions in the test data set.