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...

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...

For treatment effects—one of the core issues in modern econometric analysis—prediction and estimation are two sides of the same coin. As it turns out, machine learning methods are the tool for generalized prediction models. Combined with econometric theory, they allow us to estimate not only...

This article develops a theoretical model for evaluating mandatory activation of welfare recipients in complex activation programmes. The model aims to summarize and describe heterogeneous content that is difficult to comprehend because of local variations, staff characteristics, or other...

An important goal when analyzing the causal effect of a treatment on an outcome is to understand the mechanisms through which the treatment causally works. We define a causal mechanism effect of a treatment and the causal effect net of that mechanism using the potential outcomes framework. These...

This thesis examines causal inference related topics involving intermediate variables, and uses Bayesian methodologies to advance analysis capabilities in these areas. First, joint modeling of outcome variables with intermediate variables is considered in the context of birthweight and censored...