Sufficient Covariate, Propensity Variable and Doubly Robust Estimation

Statistical causal inference from observational studies often requires adjustment for a possibly multi-dimensional variable, where dimension reduction is crucial. The propensity score, first introduced by Rosenbaum and Rubin, is a popular approach to such reduction. We address causal inference within Dawid's decision-theoretic framework, where it is essential to pay attention to sufficient covariates and their properties. We examine the role of a propensity variable in a normal linear model. We investigate both population-based and sample-based linear regressions, with adjustments for a multivariate covariate and for a propensity variable. In addition, we study the augmented inverse probability weighted estimator, involving a combination of a response model and a propensity model. In a linear regression with homoscedasticity, a propensity variable is proved to provide the same estimated causal effect as multivariate adjustment. An estimated propensity variable may, but need not, yield better precision than the true propensity variable. The augmented inverse probability weighted estimator is doubly robust and can improve precision if the propensity model is correctly specified

A review of spatial causal inference methods for environmental and epidemiological applications

The scientific rigor and computational methods of causal inference have had great impacts on many disciplines, but have only recently begun to take hold in spatial applications. Spatial casual inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality, and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies, and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID-19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided

Eingliederungszuschüsse bei Einarbeitung und erschwerter Vermittlung: Matching-Analysen auf der Basis von Prozessdaten

Eingliederungszuschüsse bei Einarbeitung und erschwerter Vermittlung wirken sich positiv auf die Beschäftigungschancen der Geförderten aus. Dies zeigt eine aktuelle Studie des Instituts für Arbeitsmarkt- und Berufsforschung (IAB), die im Rahmen der Evaluierung der Hartz- Gesetze I-III im Auftrag des Bundesministeriums für Arbeit und Soziales in Zusammenarbeit mit dem ZEW (Mannheim) und dem IAT (Gelsenkirchen) erstellt wurde. Untersucht werden Erwerbsverläufe von Personen, die im ersten Quartal 2002 eine geförderte Beschäftigung aufgenommen haben. Die mittelfristigen Auswirkungen der Förderung lassen sich auf Basis des Vergleichs mit einer Kontrollgruppe nicht geförderter Personen schätzen. Der Anteil der Personen in regulärer Beschäftigung liegt demnach gut zwei Jahre nach Beginn der Förderung in der Gruppe der Geförderten 30 bis 50 Prozentpunkte höher als in der Vergleichsgruppe. Abstract According to a study of the Institute for Employment Research (IAB), the ZEW and the IAT settling-in allowances for job introduction as well as for hard-to-place workers have a positive impact on employment prospects of subsidized workers. The study is part of the evaluation of recent labour market reforms in Germany, financed by the German Federal Ministry for Employment and Social Affairs. We analyse individual employment careers of persons who took up subsidized work in the first three months of the year 2002. The medium-term effects of settling-in allowances are estimated by means of a matched control group of unemployed individuals. Two years after the (hypothetical) entry into subsidised work the share of employed persons is still 30 to 50 percentage points higher among subsidised workers than among non-subsidised persons in the control group

Doubly Robust-Type Estimation for Covariate Adjustment in Latent Variable Modeling

causal inference, marginal model, Monte Carlo method, propensity score, structural equation modeling,