Abstract Unmeasured confounding is one of the most important threats to the validity of observational studies. In this paper we scrutinize a recently proposed sensitivity analysis for unmeasured confounding. The analysis requires specification of two parameters, loosely defined as the maximal...

Drawing inferences about the effects of exposures or treatments is a common challenge in many scientific fields. We propose two methods serving complementary purposes in causal inference. One can be used to estimate average causal effects, assuming ``no confounding" given measured covariates....

In causal inference for longitudinal data, standard methods usually assume that the underlying processes are discrete time processes, and that the observational time points are the time points when the processes change values. The identification of these standard models often relies on the...

This paper develops a novel method for policy choice in a dynamic setting where the available data is a multi-variate time series. Building on the statistical treatment choice framework, we propose Time-series Empirical Welfare Maximization (T-EWM) methods to estimate an optimal policy rule by...

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...