Abstract Recently, Abadie et al. (Am J Polit Sci 59:495–510, 2015) have expanded synthetic control methods by the so-called cross-validation technique. We find that their results are not being reproduced when alternative software packages are used or when the variables’ ordering within the dataset is changed. We show that this failure stems from the cross-validation technique relying on non-uniquely defined predictor weights. While the amount of the resulting ambiguity is negligible for the main application of Abadie et al. (Am J Polit Sci 59:495–510, 2015), we find it to be substantial for several of their robustness analyses. Applying well-defined, standard synthetic control methods reveals that the authors’ results are particularly drive...
Building on an idea in Abadie and Gardeazabal (2003), this article investigates the application of s...
We derive conditions under which the original result of Abadie et al (2010) regarding the bias of t...
This dissertation is comprised of three essays that apply machine learning and high-dimensional stat...
While the literature on synthetic control methods mostly abstracts from out-of-sample measures, Abad...
The introduction of new “machine learning” methods and terminology to political science complicates ...
In recent years a widespread consensus has emerged about the necessity of establishing bridges betwe...
We analyze the conditions under which the Synthetic Control (SC) estimator is unbiased. We show that...
We show that a lack of guidance on how to choose the matching variables used in the Synthetic Contro...
The synthetic control method (SCM) is widely used to evaluate causal effects under quasi-experimenta...
Do controlled comparisons still have a place in comparative politics? Long criticized by quantitativ...
It is becoming increasingly popular in applications of synthetic control methods to include the enti...
The R package Synth implements synthetic control methods for comparative case studies designed to es...
This paper examines and applies of more advanced modeling methods for the time-series-cross-sectiona...
A central goal in social science is to evaluate the causal effect of a policy. One dominant approach...
The synthetic control method (SCM) has been increasingly adopted to evaluate causal effects under qu...
Building on an idea in Abadie and Gardeazabal (2003), this article investigates the application of s...
We derive conditions under which the original result of Abadie et al (2010) regarding the bias of t...
This dissertation is comprised of three essays that apply machine learning and high-dimensional stat...
While the literature on synthetic control methods mostly abstracts from out-of-sample measures, Abad...
The introduction of new “machine learning” methods and terminology to political science complicates ...
In recent years a widespread consensus has emerged about the necessity of establishing bridges betwe...
We analyze the conditions under which the Synthetic Control (SC) estimator is unbiased. We show that...
We show that a lack of guidance on how to choose the matching variables used in the Synthetic Contro...
The synthetic control method (SCM) is widely used to evaluate causal effects under quasi-experimenta...
Do controlled comparisons still have a place in comparative politics? Long criticized by quantitativ...
It is becoming increasingly popular in applications of synthetic control methods to include the enti...
The R package Synth implements synthetic control methods for comparative case studies designed to es...
This paper examines and applies of more advanced modeling methods for the time-series-cross-sectiona...
A central goal in social science is to evaluate the causal effect of a policy. One dominant approach...
The synthetic control method (SCM) has been increasingly adopted to evaluate causal effects under qu...
Building on an idea in Abadie and Gardeazabal (2003), this article investigates the application of s...
We derive conditions under which the original result of Abadie et al (2010) regarding the bias of t...
This dissertation is comprised of three essays that apply machine learning and high-dimensional stat...