Propensity score matching estimators have two advantages. One is that they overcome the curse of dimensionality of covariate matching, and the other is that they are nonparametric. However, the propensity score is usually unknown and needs to be estimated. If we estimate it nonparametrically, we...

We discuss methods for calculating multivariate normal probabilities by simulation and two new Stata programs for this purpose: -mdraws- for deriving draws from the standard uniform density using either Halton or pseudo-random sequences, and an egen function -mvnp()- for calculating the...

When considering multiple hypothesis tests simultaneously, standard statistical techniques will lead to over-rejection of null hypotheses unless the multiplicity of the testing framework is explicitly considered. In this paper we discuss the Romano-Wolf multiple hypothesis correction, and...

In this paper, we describe a computational implementation of the Synthetic difference-in-differences (SDID) estimator of Arkhangelsky et al. (2021) for Stata. Synthetic difference-in-differences can be used in a wide class of circumstances where treatment effects on some particular policy or...

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...

In this paper we evaluate the premise from the recent literature on Monte Carlo studies that an empirically motivated simulation exercise is informative about the actual ranking of various estimators when applied to a particular problem. We consider two alternative designs and provide an...

Currently there is little practical advice on which treatment effect estimator to use when trying to adjust for observable differences. A recent suggestion is to compare the performance of estimators in simulations that somehow mimic the empirical context. Two ways to run such 'empirical Monte...

Propensity score matching is a prominent strategy to reduce imbalance in observational studies. However, if imbalance is considerable and the control reservoir is small, either one has to match one control to several treated units or, alternatively, discard many treated persons. The first...

This paper builds on the Empirical Monte Carlo simulation approach developed by Huber et al. (2013) to study the estimation of Timing-of-Events (ToE) models. We exploit rich Swedish data of unemployed job-seekers with information on participation in a training program to simulate placebo...

A large part of the recent literature on program evaluation has focused on estimation of the average effect of the treatment under assumptions of unconfoundedness or ignorability following the seminal work by Rubin (1974) and Rosenbaum and Rubin (1983). In many cases however, researchers are...