As evidence from evaluation and experimental studies continue to influence decision and policymaking, applied researchers and practitioners require tools to derive valid and credible inferences. Over the past several decades, research in causal inference has progressed with the development and application of propensity scores. Since their…

Combining observational and experimental data for causal inference can improve treatment effect estimation. However, many observational data sets cannot be released due to data privacy considerations, so one researcher may not have access to both experimental and observational data. Nonetheless, a small amount of risk of disclosing sensitive…

The present paper examines a type of abstract domain-general knowledge required for the process of constructing useable domain-specific causal knowledge, the evident goal of causal learning. It tests the hypothesis that analytic knowledge of "causal-invariance decomposition functions" is essential for this process. Such knowledge…

When planning a cluster randomized trial, evaluators often have access to an enumerated cohort representing the target population of clusters. Practicalities of conducting the trial, such as the need to oversample clusters with certain characteristics in order to improve trial economy or support inferences about subgroups of clusters, may preclude…

Novel causal systems pose a problem of variable choice: How can a reasoner decide which variable is causally relevant? Which variable in the system should a learner manipulate to try to produce a desired, yet unfamiliar, casual outcome? In much causal reasoning research, participants learn how a particular set of preselected variables produce a…

Recent methods have sought to improve precision in randomized controlled trials (RCTs) by utilizing data from large observational datasets for covariate adjustment. For example, consider an RCT aimed at evaluating a new algebra curriculum, in which a few dozen schools are randomly assigned to treatment (new curriculum) or control (standard…

Teachers engaging in research has been discussed and carried out under the heuristics and methodologies of action research (Manfra, 2019; Pine, 2009). A typical action research project might involve an individual teacher studying the effectiveness of a specific instructional strategy like having students preview content before receiving direct…

The understanding and generalisation of causality are important thinking abilities, as they form the basis for a person's activity. Researchers exploring these abilities do not have a unified opinion regarding the age of children when they develop causative understanding and its determinant factors (e.g. age, prior knowledge, the content of a…

Qualitative Comparative Analysis (QCA) is a method for cross-case analyses that works best when complemented with follow-up case studies focusing on the causal quality of the solution and its constitutive terms, the underlying causal mechanisms, and potentially omitted conditions. The anchorage of QCA in set theory demands criteria for follow-up…

Recent methods to improve generalizations from nonrandom samples typically invoke assumptions such as the strong ignorability of sample selection, which is challenging to meet in practice. Although researchers acknowledge the difficulty in meeting this assumption, point estimates are still provided and used without considering alternative…

This paper investigates the association between institutional quality and generalized trust. Despite the importance of the topic, little quantitative empirical evidence exists to support either unidirectional or bidirectional causality for the reason that cross-sectional studies rarely model the reciprocal relationship between institutional…

Donald Campbell's approach to causal inference (D. T. Campbell, 1957; W. R. Shadish, T. D. Cook, & D. T. Campbell, 2002) is widely used in psychology and education, whereas Donald Rubin's causal model (P. W. Holland, 1986; D. B. Rubin, 1974, 2005) is widely used in economics, statistics, medicine, and public health. Campbell's approach focuses on…

One important property of human object categories is that they define the sets of exemplars to which newly observed properties are generalized. We manipulated the causal knowledge associated with novel categories and assessed the resulting strength of property inductions. We found that the theoretical coherence afforded to a category by…