We investigate how protectionist policies influence short-run economic growth. Our empirical strategy exploits an extraordinary tax scandal that gave rise to an unexpected change of government in Sweden. A free-trade majority in parliament was overturned by a protectionist majority in 1887. We...

Using causal graphs, this paper develops a simple check to uncover the direction of the causal link between economic policy uncertainty and stock market volatility. The check is applied to monthly data for 22 countries. The results imply that uncertainty is an instantaneous cause of stock market...

Binary treatments are often ex-post aggregates of multiple treatments or can be disaggregated into multiple treatment versions. Thus, effects can be heterogeneous due to either effect or treatment heterogeneity. We propose a decomposition method that uncovers masked heterogeneity, avoids...

One of the perceived advantages of difference-in-differences (DiD) methods is that they do not explicitly restrict how units select into treatment. However, when justifying DiD, researchers often argue that the treatment is "quasi-randomly" assigned. We investigate what selection mechanisms are...

Can one single political leader influence macroeconomic expectations on a global scale? We design a large-scale survey experiment among influential economic experts working in more than 100 countries and use the 2020 US presidential election as a quasi-natural experiment to identify the effect...

Decarbonizing the global energy matrix through investments in renewable energy (RE) is considered a pathway to mitigate the effects of global climate change. Auctions have become an increasingly popular policy instrument for this purpose. In the last few years, auctions have been rapidly adopted...

We introduce the package ddml for Double/Debiased Machine Learning (DDML) in Stata. Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal effects of endogenous variables in settings with unknown functional forms and/or...

We propose a general protocol for calibration and validation of complex simulation models by an approach based on discovery and comparison of causal structures. The key idea is that configurations of parameters of a given theoretical model are selected by minimizing a distance index between two...

Following the recent turn towards quasi-experimental approaches in the US literature on the incumbency advantage (Lee, 2001; Lee, forthcoming), we employ a Regression Discontinuity Design (RDD) to identify the causal effects of party incumbency in British and German post-World War II elections. The...

A graphical model is a graph that represents a set of conditional independence relations among the vertices (random variables). The graph is often given a causal interpretation as well. I describe how graphical causal models can be used in an algorithm for constructing partial information about...