Statistical Inference for Causal Effects

  title={Statistical Inference for Causal Effects},
  author={Fabrizia Mealli and Barbara Pacini and Donald B. Rubin},
Essays on Causal Inference for Public Policy.
Effective policymaking requires understanding the causal effects of competing proposals. Relevant causal quantities include proposals’ expected effect on different groups of recipients, the impact of
Handling parametric assumptions in principal causal effect estimation using Gaussian mixtures
  • B. Jo
  • Mathematics
    Statistics in medicine
  • 2022
Given the latent stratum membership, principal stratification models with continuous outcomes naturally fit in the parametric estimation framework of Gaussian mixtures. However, with models that are
From Controlled to Undisciplined Data: Estimating Causal Effects in the Era of Data Science Using a Potential Outcome Framework
This paper discusses the fundamental principles of causal inference - the area of statistics that estimates the effect of specific occurrences, treatments, interventions, and exposures on a given
This work proposes a Bayesian framework for record linkage and causal inference where one file comprises all the covariate and observed outcome information, and the second file consists of all individuals who receive the active treatment.
Estimación de efectos causales usando inferencia basada en el diseño para estudios observacionales que utilizan Propensity Score Matching
Las tecnicas de inferencia causal aplicadas a la evaluacion estadistica de los resultados de un experimento o estudio observacional constituyen una herramienta de vital importancia en la toma de
Industrial policy evaluation in the presence of spillovers
The shortage of studies on spatial spillovers of capital subsidy policies is rather surprising, considering that such policies are usually designed to generate spatial externalities. We propose a new
Bayesian Networks in Survey Data: Robustness and Sensitivity Issues
This paper focuses on the selection of a robust network structure according to different learning algorithms and the measure of arc strength using resampling techniques and shows how ‘what-if’ sensitivity scenarios are generated with BN using hard and soft evidence in the framework of predictive inference.
On generating high InfoQ with Bayesian networks
Examples from customer surveys of high tech companies, risk management of telecom systems, monitoring of bioreactors and managing healthcare of diabetic patients support the more general claim made here that Bayesian networks generate high information quality (InfoQ).
The effects of a dropout prevention program on secondary students’ outcomes
Innovare is a teacher-based dropout prevention program, promoted by the Regional government in Tuscany (Italy), aimed at reducing dropout in the early grades of vocational high schools through the


Alternative Estimates of the Effect of Family Structure during Adolescence on High School Graduation
Abstract Many studies have reported significant empirical associations between family structure during childhood and children's outcomes later in life. It may be that living in a nonintact family has
Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome
This paper proposes a simple technique for assessing the range of plausible causal con- clusions from observational studies with a binary outcome and an observed categorical covariate. The technique
Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score
Abstract Matched sampling is a method for selecting units from a large reservoir of potential controls to produce a control group of modest size that is similar to a treated group with respect to the
Likelihood-Based Analysis of Causal Effects of Job-Training Programs Using Principal Stratification
Government-sponsored job-training programs must be subject to evaluation to assess whether their effectiveness justifies their cost to the public. The evaluation usually focuses on employment and
Bayesian inference for causal effects in randomized experiments with noncompliance
For most of this century, randomization has been a cornerstone of scientific experimentation, especially when dealing with humans as experimental units. In practice, however, noncompliance is