DR-VIDAL - Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation

Determining causal effects of interventions onto outcomes from observational (non-randomized) data, e.g., treatment repurposing using electronic health records, is challenging due to underlying bias. Causal deep learning has improved over traditional techniques for estimating individualized treatment effects. We present the Doubly Robust Variational Information-theoretic Deep Adversarial Learning (DR-VIDAL), a novel generative framework that combines two joint models of treatment and outcome, ensuring an unbiased estimation even when one of the two is misspecified. DR-VIDAL uses a variational autoencoder (VAE) to factorize confounders into latent variables according to causal assumptions; then, an information-theoretic generative adversarial network (Info-GAN) is used to generate counterfactuals; finally, a doubly robust block incorporates propensity matching/weighting into predictions. On synthetic and real-world datasets, DR-VIDAL achieves better performance than other non-generative and generative methods. In conclusion, DR-VIDAL uniquely fuses causal assumptions, VAE, Info-GAN, and doubly robustness into a comprehensive, performant framework. Code is available at: https://bitbucket.org/goingdeep2406/dr-vidal/src/master/

How “is” shapes “ought” for folk-biological concepts

Knowing which features are frequent among a biological kind (e.g., that most zebras have stripes) shapes people’s representations of what category members are like (e.g., that typical zebras have stripes) and normative judgments about what they ought to be like (e.g., that zebras should have stripes). In the current work, we ask if people’s inclination to explain why features are frequent is a key mechanism through which what “is” shapes beliefs about what “ought” to be. Across four studies (N = 591), we find that frequent features are often explained by appeal to feature function (e.g., that stripes are for camouflage), that functional explanations in turn shape judgments of typicality, and that functional explanations and typicality both predict normative judgments that category members ought to have functional features. We also identify the causal assumptions that license inferences from feature frequency and function, as well as the nature of the normative inferences that are drawn: by specifying an instrumental goal (e.g., camouflage), functional explanations establish a basis for normative evaluation. These findings shed light on how and why our representations of how the natural world is shape our judgments of how it ought to be.

Recommendations for improving causal inference of mediation analysis

Mediation analysis is an indispensable tool for investigating how a treatment causally affects an outcome via intermediate variables. Recently, there have been increased concerns about the validity of causal inferences in conclusions drawn using mediation analysis. However, the discussions are limited to a single mediator, and importantly, there is a lack of guidelines on substantiating causal inferences. In this article, we first provide a thorough examination of the causal assumptions underpinning mediation analysis. We pay particular attention to the practice of exploring mediated effects along various paths linking several mediators and the stringent -- yet often overlooked -- assumptions that predicate valid inference. To mitigate the risk of invalid inference, we introduce an alternative approach focusing on mediator-specific indirect effects. An appealing feature of this approach is that valid causal inference of mediation analysis with multiple mediators does not necessitate assuming a (correct) causal structure among the mediators. Finally, we provide a practical guide to improve the research practice of mediation analysis. We clarify when mediation analysis is (in)appropriate; when appropriate, we recommend that researchers preregister (i) justifications of the asserted causal relations in their mediation analysis, (ii) pre-treatment confounders, and (iii) the either path- or mediator-specific indirect effects to be investigated. Confounding adjustment when estimating the preregistered indirect effects and sensitivity analyses for unmeasured confounding can fortify causal inferences in the conclusions. We hope this article will encourage explication, justification, and reflection of the causal assumptions underpinning mediation analysis to improve the validity of causal inferences in psychology research.

Improving causal inference of mediation analysis with multiple mediators in randomized experiments

Mediation analysis is an essential tool for investigating how a treatment causally affects an outcome via intermediate variables. However, violations of the (often implicit) causal assumptions can severely threaten the validity of causal inferences of mediation analysis. Psychologists have recently started to raise such concerns, but the discussions have been limited to mediation analysis with a single mediator. In this article, we examine the causal assumptions when there are multiple possible mediators. We pay particular attention to the practice of exploring mediated effects along various paths linking several mediators. Substantive conclusions using such methods are predicated on stringent assumptions about the underlying causal structure that can be indefensible in practice. Therefore, we recommend that researchers shift focus to mediator-specific indirect effects using a recently proposed framework of interventional (in)direct effects. A vital benefit of this approach is that valid causal inference of mediation analysis with multiple mediators does not necessitate correctly assuming the underlying causal structure among the mediators. Finally, we provide a practical guide with suggestions to improve the research practice of mediation analysis at each study stage. We hope this article will encourage explication, justification, and reflection of the causal assumptions underpinning mediation analysis to improve the validity of causal inferences in psychology research.

THE RISE AND FALL OF THE WESSELY SCHOOL

The Wessely School’s (WS) approach to medically unexplained symptoms, myalgic encephalomyelitis and chronic fatigue syndrome (MUS/MECFS) is critically reviewed using scientific criteria. Based on the ‘Biopsychosocial Model’, the WS proposes that patients’ dysfunctional beliefs, deconditioning and attentional biases cause illness, disrupt therapies, and lead to preventable deaths. The evidence reviewed here suggests that none of the WS hypotheses is empirically supported. The lack of robust supportive evidence, fallacious causal assumptions, inappropriate and harmful therapies, broken scientific principles, repeated methodological flaws and unwillingness to share data all give the appearance of cargo cult science. The WS approach needs to be replaced by an evidence-based, biologically-grounded, scientific approach to MUS/MECFS.

Quantifying the contribution of occupational segregation to racial disparities in health: A gap-closing perspective

Racism causes racial disparities in health, and structural racism has many components. Focusing on one of those components, this paper addresses occupational segregation. I document high onset of work-limiting disabilities in occupations where many workers identify as non-Hispanic Black or as Hispanic. I then pivot to a causal question. Suppose we took a sample from the population and reassigned their occupations to be a function of education alone. To what degree would health disparities narrow for that sample? Using observational data, I estimate that the disparity between non-Hispanic Black and white workers would narrow by one-third. This estimate is credible because of adjustment for lagged measures of demographics, human capital, and health carried out under transparent causal assumptions. The result contributes to understanding about inequality and health by quantifying the contribution of occupational segregation to a disparity: if we took a sample and reassigned occupations, the disparity would narrow but would not disappear. The paper contributes to methodology by illustrating an approach to macro-level claims (how segregation affects a population disparity) that draws on explicitly causal micro-level analyses (potential outcomes for individuals) for which data are abundant.

Mediation effects that emulate a target randomised trial: Simulation-based evaluation of ill-defined interventions on multiple mediators

Many epidemiological questions concern potential interventions to alter the pathways presumed to mediate an association. For example, we consider a study that investigates the benefit of interventions in young adulthood for ameliorating the poorer mid-life psychosocial outcomes of adolescent self-harmers relative to their healthy peers. Two methodological challenges arise. First, mediation methods have hitherto mostly focused on the elusive task of discovering pathways, rather than on the evaluation of mediator interventions. Second, the complexity of such questions is invariably such that there are no well-defined mediator interventions (i.e. actual treatments, programs, etc.) for which data exist on the relevant populations, outcomes and time-spans of interest. Instead, researchers must rely on exposure (non-intervention) data, that is, on mediator measures such as depression symptoms for which the actual interventions that one might implement to alter them are not well defined. We propose a novel framework that addresses these challenges by defining mediation effects that map to a target trial of hypothetical interventions targeting multiple mediators for which we simulate the effects. Specifically, we specify a target trial addressing three policy-relevant questions, regarding the impacts of hypothetical interventions that would shift the mediators’ distributions (separately under various interdependence assumptions, jointly or sequentially) to user-specified distributions that can be emulated with the observed data. We then define novel interventional effects that map to this trial, simulating shifts by setting mediators to random draws from those distributions. We show that estimation using a g-computation method is possible under an expanded set of causal assumptions relative to inference with well-defined interventions, which reflects the lower level of evidence that is expected with ill-defined interventions. Application to the self-harm example in the Victorian Adolescent Health Cohort Study illustrates the value of our proposal for informing the design and evaluation of actual interventions in the future.

Biodiversity narratives: stories of the evolving conservation landscape

SummaryNarratives shape human understanding and underscore policy, practice and action. From individuals to multilateral institutions, humans act based on collective stories. As such, narratives have important implications for revisiting biodiversity. There have been growing calls for a ‘new narrative’ to underpin efforts to address biodiversity decline that, for example, foreground optimism, a more people-centred narrative or technological advances. This review presents some of the main contemporary narratives from within the biodiversity space to reflect on their underpinning categories, myths and causal assumptions. It begins by reviewing various interpretations of narrative, which range from critical views where narrative is a heuristic for understanding structures of domination, to advocacy approaches where it is a tool for reimagining ontologies and transitioning to sustainable futures. The work reveals how the conservation space is flush with narratives. As such, efforts to search for a ‘new narrative’ for conservation can be usefully informed by social science scholarship on narratives and related constructs and should reflect critically on the power of narrative to entrench old ways of thought and practice and, alternatively, make space for new ones. Importantly, the transformative potential of narrative may not lie in superficial changes in messaging, but in using narrative to bring multiple ways of knowing into productive dialogue to revisit biodiversity and foster critical reflection.