Program

Recently, several papers have appeared that apply causal inference techniques to cross-lagged panel research, explicating the assumptions under which cross-lagged parameters can be given a causal interpretation. An important prerequisite for such formal analyses is that there is a presumed causal structure; this implies researchers have already had to settle on whether there may be unobserved stable between-person differences that confound the causal effect of interest. However, the latter still forms a major point of dispute in panel research. In this talk, I will examine the within-between distinction in panel research from three different perspectives: design, data, and research question. This will show the importance of considering the timescale at which a process operates, and how this relates to the design that is used and the stability patterns that characterize the data. Moreover, it highlights the need to carefully (re)consider our research question, as well as possible alternative designs. 

Neuroscientists are increasingly interested in using neuroimaging data to make causal statements about brain function. Often methods for modeling associations among variables are used to make causal inferences, with associations taken to evidence causation.  Here we discuss several approaches towards estimating causal effects in studies of human brain function. This includes a multi-subject whole brain causal model that allows for the estimation of task-related effects, and an approach towards high-dimensional mediation analysis that allows for the determination of the brain regions that mediate the relationship between an exposure and an outcome.

Each person is unique, but this is not reflected in cross-sectional or traditional longitudinal study designs, or in analyses that treat individuals as representative members of a group (e.g., by gender). Intensive longitudinal data (e.g., from daily diary or ecological momentary assessment studies) are necessary to accurately reflect person-specificity, and network approaches hold significant promise for their analysis. A network approach considers behaviors as nodes and relations among them as edges, and it instigates questions about causality via edge temporality and directionality. This presentation will consider those questions with respect to group iterative multiple model estimation (GIMME), a person-specific network analysis approach that generates directed, time-indexed, and even sample-level edges among behavioral nodes uniquely for each person in a sample. GIMME will be conceptually and mathematically described, its implementation briefly discussed, and its potential illustrated through an application to 75-day intensive longitudinal data on gender. 

In recent years, researchers have pushed for increased transparency about the research process as a means to improve science. In this talk, Julia Rohrer is going to argue that one area of transparency has been neglected: transparency about the goals of statistical analyses. Such transparency is all the more important the more complex and "impressive" statistical models get. Demanding that every quantitative empirical journal article explicitly states the theoretical estimand of interest, as well as the identifying assumptions linking the theoretical estimand to the statistical analysis, could make it much easier for readers to critically evaluate studies, but also push researchers to do better research in the first place. 

Finding causes is a central goal in psychological research. In this talk, I discuss the

problems and challenges in finding psychological causes from the perspective of philosophy

of science, related to (1) the ill-defined nature of most psychological constructs, (2) the

difficulties in manipulating psychological constructs and measuring them in a robust way,

and (3) failures of causal sufficiency (also known as the problem of unmeasured common

causes). I will then expand this discussion to the context of longitudinal data analysis, and

consider how these problems manifest there and what additional problems arise. Although

the conclusions will be rather pessimistic and critical I will end by discussing several

alternative approaches and ways forward.