Research in the social and behavioral sciences relies on a wide range of experimental and quasi-experimental designs to estimate the causal effects of specific programs, policies, and events. In this paper we highlight measurement issues relevant to evaluating the validity of causal estimation and generalization. These issues impact all four…

Media articles often communicate the latest scientific findings, and readers must evaluate the evidence and consider its potential implications. Prior work has found that the inclusion of graphs makes messages about scientific data more persuasive (Tal & Wansink, 2016). One explanation for this finding is that such visualizations evoke the…

Conventional advice discourages controlling for postoutcome variables in regression analysis. By contrast, we show that controlling for commonly available postoutcome (i.e., future) values of the treatment variable can help detect, reduce, and even remove omitted variable bias (unobserved confounding). The premise is that the same unobserved…

The impact of a major historical event on child and youth development has been of great interest in the study of the life course. This study is focused on assessing the causal effect of the Great Recession on youth disconnection from school and work. Building on the insights offered by the age-period-cohort research, econometric methods, and…

Beliefs about the controllability of positive or negative events in the environment can shape learning throughout the lifespan. Previous research has shown that adults' learning is modulated by beliefs about the causal structure of the environment such that they update their value estimates to a lesser extent when the outcomes can be attributed to…

This article reports results from two studies of how people answer counterfactual questions about simple machines. Participants learned about devices that have a specific configuration of components, and they answered questions of the form "If component X had not operated [failed], would component Y have operated?" The data from these…

Three experiments examined children's and adults' abilities to use statistical and temporal information to distinguish between common cause and causal chain structures. In Experiment 1, participants were provided with conditional probability information and/or temporal information and asked to infer the causal structure of a 3-variable mechanical…

Purpose: The purpose of this paper is to conduct a critical review of the mediation studies published in the field of Human Resource Development (HRD) to discern if the study designs, the nature of data collection and the choice of statistical methods justify the causal claims made in those studies. Design/methodology/approach: This paper conducts…

Organizational and applied sciences have long struggled with improving causal inference in quasi-experiments. We introduce organizational researchers to propensity scoring, a statistical technique that has become popular in other applied sciences as a means for improving internal validity. Propensity scoring statistically models how individuals in…

A fundamental issue for theories of human induction is to specify constraints on potential inferences. For inferences based on shared category membership, an analogy, and/or a relational schema, it appears that the basic goal of induction is to make accurate and goal-relevant inferences that are sensitive to uncertainty. People can use source…

Legitimate knowledge claims about causation have been a central concern among evaluators and applied researchers for several decades and often have been the subject of heated debates. In recent years these debates have resurfaced with a renewed intensity, due in part to the priority currently being given to randomized experiments by many funders…

Causal reasoning represents one of the most basic and important cognitive processes that underpin all higher-order activities, such as conceptual understanding and problem solving. Hume called causality the "cement of the universe" [Hume (1739/2000). Causal reasoning is required for making predictions, drawing implications and…

"Theory as Method: Small Theories of Treatments," by Mark W. Lipsey, is one of the most influential and highly cited articles to appear in "New Directions for Evaluation." It articulated an approach in which methods for studying causation depend, in large part, on what is known about the theory underlying the program. Lipsey discussed the benefits…

This paper comments on the articles by Cheng and by Novick and Cheng. It has been claimed that the power PC theory reconciles regularity and power theories of causal judgment by showing how contingency information is used for inferences about unobservable causal powers. Under the causal powers theory causal relations are understood as generative…

Recent research suggests that outcome additivity pretraining modulates blocking in human causal learning. However, the existing evidence confounds outcome additivity and outcome maximality. Here the authors present evidence for the influence of presenting information about outcome maximality (Experiment 1) and outcome additivity (Experiment 2) on…