Recent researches in econometrics and statistics have gained considerable insights into the use of instrumental variables (IVs) for causal inference. A basic idea is that IVs serve as an experimental handle, the turning of which may change each individual’s treatment status and, through and only through this effect, also change observed outcome. The average difference in observed outcome relati...

Among the complex large quantity of sports phenomenon and sports issues, we need to discuss the causal relationship and essential rule in between. Mathematical statistics is one of the common and practical methods for causal inference. In order to better understand, select, and use mathematical statistic methods, and to avoid deviation of research result due to misuse or even abuse of statistic...

Much research in the social sciences attempts to estimate the effect of some intervention or “treatment” such as a school dropout prevention program or television watching. However, particularly in the social sciences, it is generally not possible to randomly assign units to receive the treatment condition or the control condition, and thus the resulting data are observational, where we simply ...

I begin this discussion by thanking Larry Hedges, the editor of the journal, for giving me an opportunity to provide a commentary on this stimulating article. I also would like to congratulate the authors of the article for their insightful discussion on causal mediation analysis, which is one of the most important and challenging methodological problems we face in the literature of causal infe...

In this technical note we compare the performance of four gradient-free MCMC samplers (random walk Metropolis sampling, slice-sampling, adaptive MCMC sampling and population-based MCMC sampling with tempering) in terms of the number of independent samples they can produce per unit computational time. For the Bayesian inversion of a single-node neural mass model, both adaptive and population-bas...

We present two algorithms for exact and ap­ proximate inference in causal networks. The first algorithm, dynamic conditioning, is a re­ finement of cutset conditioning that has lin­ ear complexity on some networks for which cutset conditioning is exponential. The sec­ ond algorithm, B-conditioning, is an algo­ rithm for approximate inference that allows one to trade-off the quality of approxima...

Background Over the past 50 years, ILSAs have experienced marked development in terms of scope, participants, and sophistication. The first such study, the International Association for the Evaluation of Educational Achievement’s Pilot Twelve Country Study was completed in 1961 and measured achievement in math, reading, geography, science, and so-called non-verbal ability (Forshay et al. 1962)....

This paper provides a brief overview to four major types of causal models for health-sciences research: Graphical models (causal diagrams), potential-outcome (counterfactual) models, sufficient-component cause models, and structural-equations models. The paper focuses on the logical connections among the different types of models and on the different strengths of each approach. Graphical models...

We describe TimeSleuth, a hybrid tool based on the C4.5 classification software, which is intended for the discovery of temporal/causal rules. Temporally ordered data are gathered from observable attributes of a system, and used to discover relations among the attributes. In general, such rules could be atemporal or temporal. We evaluate TimeSleuth using synthetic data sets with well-known caus...

In this paper we propose methods for estimating heterogeneity in causal effects in experimental and observational studies, and for conducting inference about the magnitude of the differences in treatment effects across subsets of the population. In applications, our method provides a data-driven approach to determine which subpopulations have large or small treatment effects and to test hypothe...