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There are widespread assumptions that implicit group bias leads to biased behavior. This chapter summarizes existing evidence on the link between implicit group bias and biased behavior, with an analysis of the strength of that evidence for causality. Our review leads to the conclusion that although there is substantial evidence that implicit group bias is related to biased behavior, claims about causality are not currently supported. With plausible alternative explanations for observed associations, as well as the possibility of reverse causation, scientists and policy makers need to be careful about claims made and actions taken to address discrimination, based on the assumption that implicit bias is the problem.
Mediation analysis practices in social and personality psychology would benefit from the integration of practices from statistical mediation analysis, which is currently commonly implemented in social and personality psychology, and causal mediation analysis, which is not frequently used in psychology. In this chapter, I briefly describe each method on its own, then provide recommendations for how to integrate practices from each method to simultaneously evaluate statistical inference and causal inference as part of a single analysis. At the end of the chapter, I describe additional areas of recent development in mediation analysis that that social and personality psychologists should also consider adopting I order to improve the quality of inference in their mediation analysis: latent variables and longitudinal models. Ultimately, this chapter is meant to be a kind introduction to causal inference in the context of mediation with very practical recommendations for how one can implement these practices in one’s own research.
Field research refers to research conducted with a high degree of naturalism. The first part of this chapter provides a definition of field research and discusses advantages and limitations. We then provide a brief overview of observational field research methods, followed by an in-depth overview of experimental field research methods. We discuss randomization schemes of different types in field experimentation, such as cluster randomization, block randomization, and randomized rollout or waitlist designs, as well as statistical implementation concerns when conducting field experiments, including spillover, attrition, and noncompliance. The second part of the chapter provides an overview of important considerations when conducting field research. We discuss the psychology of construal in the design of field research, conducting non-WEIRD field research, replicability and generalizability, and how technological advances have impacted field research. We end by discussing career considerations for psychologists who want to get involved in field research.
A quasi-experiment is a type of study that attempts to mimic the objectives and structure of traditional (randomized) experiments. However, quasi-experiments differ from experiments in that condition assignment is randomized in experiments whereas it is not randomized in quasi-experiments. This chapter reviews conceptual, methodological, and practical issues that arise in the design, implementation, and interpretation of quasi-experiments. The chapter begins by highlighting similarities and differences between quasi-experiments, randomized experiments, and nonexperimental studies. Next, it provides a framework for discussion of the relative strengths and weaknesses of different study types. The chapter then discusses traditional threats to causal inferences when conducting studies of different types and reviews the most common quasi-experimental designs and how they attempt to reach accurate assessments of the causal impact of independent variables. The chapter concludes with a discussion of how quasi-experiments might be integrated with studies of other types to produce richer insights.
Quantifying the causal effects of race is one of the more controversial and consequential endeavors to have emerged from the causal revolution in the social sciences. The predominant view within the causal inference literature defines the effect of race as the effect of race perception and commonly equates this effect with “disparate treatment” racial discrimination. If these concepts are indeed equivalent, the stakes of these studies are incredibly high as they stand to establish or discredit claims of discrimination in courts, policymaking circles and public opinion. This paper interrogates the assumptions upon which this enterprise has been built. We ask: what is a perception of race, a perception of, exactly? Drawing on a rich tradition of work in critical race theory and social psychology on racial cognition, we argue that perception of race and perception of other decision-relevant features of an action situation are often co-constituted; hence, efforts to distinguish and separate these effects from each other are theoretically misguided. We conclude that empirical studies of discrimination must turn to defining what constitutes just treatment in light of the social differences that define race.
Observational studies consistently report associations between tobacco use, cannabis use and mental illness. However, the extent to which this association reflects an increased risk of new-onset mental illness is unclear and may be biased by unmeasured confounding.
Methods
A systematic review and meta-analysis (CRD42021243903). Electronic databases were searched until November 2022. Longitudinal studies in general population samples assessing tobacco and/or cannabis use and reporting the association (e.g. risk ratio [RR]) with incident anxiety, mood, or psychotic disorders were included. Estimates were combined using random-effects meta-analyses. Bias was explored using a modified Newcastle–Ottawa Scale, confounder matrix, E-values, and Doi plots.
Results
Seventy-five studies were included. Tobacco use was associated with mood disorders (K = 43; RR: 1.39, 95% confidence interval [CI] 1.30–1.47), but not anxiety disorders (K = 7; RR: 1.21, 95% CI 0.87–1.68) and evidence for psychotic disorders was influenced by treatment of outliers (K = 4, RR: 3.45, 95% CI 2.63–4.53; K = 5, RR: 2.06, 95% CI 0.98–4.29). Cannabis use was associated with psychotic disorders (K = 4; RR: 3.19, 95% CI 2.07–4.90), but not mood (K = 7; RR: 1.31, 95% CI 0.92–1.86) or anxiety disorders (K = 7; RR: 1.10, 95% CI 0.99–1.22). Confounder matrices and E-values suggested potential overestimation of effects. Only 27% of studies were rated as high quality.
Conclusions
Both substances were associated with psychotic disorders and tobacco use was associated with mood disorders. There was no clear evidence of an association between cannabis use and mood or anxiety disorders. Limited high-quality studies underscore the need for future research using robust causal inference approaches (e.g. evidence triangulation).
This chapter focuses on causal inference in healthcare, emphasizing the need to identify causal relationships in data to answer important questions related to efficacy, mortality, productivity, and care delivery models. The authors discuss the limitations of randomized controlled trials due to ethical or pragmatic considerations and introduce quasi-experimental research designs as a scientifically coherent alternative. They divide these designs into two broad categories, independence-based designs and model-based designs, and explain the validity of assumptions necessary for each design. The chapter covers key concepts such as potential outcomes, selection bias, heterogeneous treatment effects bias, average treatment effect, average treatment effect for the treated and untreated, and local average treatment effect. Additionally, it discusses important quasi-experimental designs such as regression discontinuity, difference-in-differences, and synthetic controls. The chapter concludes by highlighting the importance of careful selection and application of these methods to estimate causal effects accurately and open the black box of healthcare.
What was the effect of war outcomes on key indicators of state formation in a post-war phase? In this chapter I demonstrate that victors and losers of war were set into different state capacity trajectories after war outcomes were revealed. I do this using a set of cutting-edge causal inference techniques to analyse the gap in state capacity that was generated between winners and losers in the time-period of most stringent warfare (1865-1913). After substantiating that the outcomes of these wars were determined by exogenous or fortuitous events, I provide a short description of my treatment—i.e., defeat—and outcomes—i.e., total revenues and railroad mileage as key indicators of state infrastructural capacity. My estimator, a difference-in-differences model, shows defeat had a negative long-term impact on state capacity which remains remarkably robust even after relaxing key assumptions. Finally, I use the synthetic control method to estimate how state capacity in Paraguay and Peru would have evolved in a counterfactual world where these countries were spared the most severe defeats in late nineteenth-century Latin America.
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Part III
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Methodological Challenges of Experimentation in Sociology
Davide Barrera, Università degli Studi di Torino, Italy,Klarita Gërxhani, Vrije Universiteit, Amsterdam,Bernhard Kittel, Universität Wien, Austria,Luis Miller, Institute of Public Goods and Policies, Spanish National Research Council,Tobias Wolbring, School of Business, Economics and Society at the Friedrich-Alexander-University Erlangen-Nürnberg
This chapter addresses the often-misunderstood concept of validity. Much of the methodological discussion around sociological experiments is framed in terms of internal and external validity. The standard view is that the more we ensure that the experimental treatment is isolated from potential confounds (internal validity), the more unlikely it is that the experimental results can be representative of phenomena of the outside world (external validity). However, other accounts describe internal validity as a prerequisite of external validity: Unless we ensure internal validity of an experiment, little can be said of the outside world. We contend in this chapter that problems of either external or internal validity do not necessarily depend on the artificiality of experimental settings or on the laboratory–field distinction between experimental designs. We discuss the internal–external distinction and propose instead a list of potential threats to the validity of experiments that includes "usual suspects" like selection, history, attrition, and experimenter demand effects and elaborate on how these threats can be productively handled in experimental work. Moreover, in light of the different types of experiments, we also discuss the strengths and weaknesses of each regarding threats to internal and external validity.
Synthetic controls (SCs) are widely used to estimate the causal effect of a treatment. However, they do not account for the different speeds at which units respond to changes. Reactions may be inelastic or “sticky” and thus slower due to varying regulatory, institutional, or political environments. We show that these different reaction speeds can lead to biased estimates of causal effects. We therefore introduce a dynamic SC approach that accommodates varying speeds in time series, resulting in improved SC estimates. We apply our method to re-estimate the effects of terrorism on income (Abadie and Gardeazabal [2003, American Economic Review 93, 113–132]), tobacco laws on consumption (Abadie, Diamond, and Hainmueller [2010, Journal of the American Statistical Association 105, 493–505]), and German reunification on GDP (Abadie, Diamond, and Hainmueller [2015, American Journal of Political Science 59, 495–510]). We also assess the method’s performance using Monte Carlo simulations. We find that it reduces errors in the estimates of true treatment effects by up to 70% compared to traditional SCs, improving our ability to make robust inferences. An open-source R package, dsc, is made available for easy implementation.
The human sciences should seek generalisations wherever possible. For ethical and scientific reasons, it is desirable to sample more broadly than ‘Western, educated, industrialised, rich, and democratic’ (WEIRD) societies. However, restricting the target population is sometimes necessary; for example, young children should not be recruited for studies on elderly care. Under which conditions is unrestricted sampling desirable or undesirable? Here, we use causal diagrams to clarify the structural features of measurement error bias and target population restriction bias (or ‘selection restriction’), focusing on threats to valid causal inference that arise in comparative cultural research. We define any study exhibiting such biases, or confounding biases, as weird (wrongly estimated inferences owing to inappropriate restriction and distortion). We explain why statistical tests such as configural, metric and scalar invariance cannot address the structural biases of weird studies. Overall, we examine how the workflows for causal inference provide the necessary preflight checklists for ambitious, effective and safe comparative cultural research.
Causal inference requires contrasting counterfactual states under specified interventions. Obtaining these contrasts from data depends on explicit assumptions and careful, multi-step workflows. Causal diagrams are crucial for clarifying the identifiability of counterfactual contrasts from data. Here, I explain how to use causal directed acyclic graphs (DAGs) to determine if and how causal effects can be identified from non-experimental observational data, offering practical reporting tips and suggestions to avoid common pitfalls.
Confounding bias arises when a treatment and outcome share a common cause. In randomised controlled experiments (trials), treatment assignment is random, ostensibly eliminating confounding bias. Here, we use causal directed acyclic graphs to unveil eight structural sources of bias that nevertheless persist in these trials. This analysis highlights the crucial role of causal inference methods in the design and analysis of experiments, ensuring the validity of conclusions drawn from experimental data.
Healthcare staff use coercive measures to manage patients at acute risk of harm to self or others, but their effect on patients’ mental health is underexplored. This nationwide Swiss study emulated a trial to investigate the effects of coercive measures on the mental health of psychiatric inpatients at discharge.
Methods
We analysed retrospective longitudinal data from all Swiss adult psychiatric hospitals that provided acute care (2019–2021). The primary exposure was any coercive measure during hospitalization; secondary exposures were seclusion, restraint and forced medication. Our primary outcome was Health of the Nations Outcome Scale (HoNOS) score at discharge. We used inverse probability of treatment weighting to emulate random assignment to the exposure.
Results
Of 178,369 hospitalizations, 9.2% (n = 18,800) included at least one coercive measure. In patients exposed to coercive measures, mental health worsened a small but statistically significant amount more than in non-exposed patients. Those who experienced at least one coercive measure during hospitalization had a significantly higher HoNOS score (1.91-point, p < .001, 95% confidence interval [CI]: 1.73; 2.09) than those who did not experience any coercive measure. Results were similar for seclusion (1.60-point higher score, p < .001, 95% CI: 1.40; 1.79) and forced medication (1.97-point higher score, p < .001, 95% CI: 1.65; 2.30). Restraint had the strongest effect (2.83-point higher score, p < .001, 95% CI: 2.38; 3.28).
Conclusions
Our study presents robust empirical evidence highlighting the detrimental impact of coercive measures on the mental health of psychiatric inpatients. It underscores the importance of avoiding these measures in psychiatric hospitals and emphasized the urgent need for implementing alternatives in clinical practice.
Inverse probability weighting is a common remedy for missing data issues, notably in causal inference. Despite its prevalence, practical application is prone to bias from propensity score model misspecification. Recently proposed methods try to rectify this by balancing some moments of covariates between the target and weighted groups. Yet, bias persists without knowledge of the true outcome model. Drawing inspiration from the quasi maximum likelihood estimation with misspecified statistical models, I propose an estimation method minimizing a distance between true and estimated weights with possibly misspecified models. This novel approach mitigates bias and controls mean squared error by minimizing their upper bounds. As an empirical application, it gives new insights into the study of foreign occupation and insurgency in France.
Exposure to second-generation antipsychotics (SGAs) carries a risk of type 2 diabetes, but questions remain about the diabetogenic effects of SGAs.
Aims
To assess the diabetes risk associated with two frequently used SGAs.
Method
This was a retrospective cohort study of adults with schizophrenia, bipolar I disorder or severe major depressive disorder (MDD) exposed during 2008–2013 to continuous monotherapy with aripiprazole or olanzapine for up to 24 months, with no pre-period exposure to other antipsychotics. Newly diagnosed type 2 diabetes was quantified with targeted minimum loss-based estimation; risk was summarised as the restricted mean survival time (RMST), the average number of diabetes-free months. Sensitivity analyses were used to evaluate potential confounding by indication.
Results
Aripiprazole-treated patients had fewer diabetes-free months compared with olanzapine-treated patients. RMSTs were longer in olanzapine-treated patients, by 0.25 months [95% CI: 0.14, 0.36], 0.16 months [0.02, 0.31] and 0.22 months [0.01, 0.44] among patients with schizophrenia, bipolar I disorder and severe MDD, respectively. Although some sensitivity analyses suggest a risk of unobserved confounding, E-values indicate that this risk is not severe.
Conclusions
Using robust methods and accounting for exposure duration effects, we found a slightly higher risk of type 2 diabetes associated with aripiprazole compared with olanzapine monotherapy regardless of diagnosis. If this result was subject to unmeasured selection despite our methods, it would suggest clinician success in identifying olanzapine candidates with low diabetes risk. Confirmatory research is needed, but this insight suggests a potentially larger role for olanzapine in the treatment of well-selected patients, particularly for those with schizophrenia, given the drug's effectiveness advantage among them.
This chapter moves from regression to methods that focus on the pattern presented by multiple variables, albeit with applications in regression analysis. A strong focus is to find patterns that beg further investigation, and/or replace many variables by a much smaller number that capture important structure in the data. Methodologies discussed include principal components analysis and multidimensional scaling more generally, cluster analysis (the exploratory process that groups “alike” observations) and dendogram construction, and discriminant analysis. Two sections discuss issues for the analysis of data, such as from high throughput genomics, where the aim is to determine, from perhaps thousands or tens of thousands of variables, which are shifted in value between groups in the data. A treatment of the role of balance and matching in making inferences from observational data then follows. The chapter ends with a brief introduction to methods for multiple imputation, which aims to use multivariate relationships to fill in missing values in observations that are incomplete, allowing them to have at least some role in a regression or other further analysis.
While past research suggested that living arrangements are associated with suicide death, no study has examined the impact of sustained living arrangements and the change in living arrangements. Also, previous survival analysis studies only reported a single hazard ratio (HR), whereas the actual HR may change over time. We aimed to address these limitations using causal inference approaches.
Methods
Multi-point data from a general Japanese population sample were used. Participants reported their living arrangements twice within a 5-year time interval. After that, suicide death, non-suicide death and all-cause mortality were evaluated over 14 years. We used inverse probability weighted pooled logistic regression and cumulative incidence curve, evaluating the association of time-varying living arrangements with suicide death. We also studied non-suicide death and all-cause mortality to contextualize the association. Missing data for covariates were handled using random forest imputation.
Results
A total of 86,749 participants were analysed, with a mean age (standard deviation) of 51.7 (7.90) at baseline. Of these, 306 died by suicide during the 14-year follow-up. Persistently living alone was associated with an increased risk of suicide death (risk difference [RD]: 1.1%, 95% confidence interval [CI]: 0.3–2.5%; risk ratio [RR]: 4.00, 95% CI: 1.83–7.41), non-suicide death (RD: 7.8%, 95% CI: 5.2–10.5%; RR: 1.56, 95% CI: 1.38–1.74) and all-cause mortality (RD: 8.7%, 95% CI: 6.2–11.3%; RR: 1.60, 95% CI: 1.42–1.79) at the end of the follow-up. The cumulative incidence curve showed that these associations were consistent throughout the follow-up. Across all types of mortality, the increased risk was smaller for those who started to live with someone and those who transitioned to living alone. The results remained robust in sensitivity analyses.
Conclusions
Individuals who persistently live alone have an increased risk of suicide death as well as non-suicide death and all-cause mortality, whereas this impact is weaker for those who change their living arrangements.
Theories propose that judgment of and reactivity to inner experiences are mediators of the effect of mindfulness-based interventions on generalized anxiety disorder (GAD). However, no study has tested such theories using brief, mindfulness ecological momentary intervention (MEMI). We thus tested these theories using a 14-day MEMI versus self-monitoring app (SM) control for GAD.
Methods
Participants (N = 110) completed self-reports of trait mindfulness (Five Facet Mindfulness Questionnaire), GAD severity (GAD-Questionnaire-IV), and trait perseverative cognitions (Perseverative Cognitions Questionnaire) at prerandomization, posttreatment, and 1-month follow-up (1MFU). Counterfactual mediation analyses with temporal precedence were conducted.
Results
Improvement in pre–post mindfulness domains (acceptance of emotions, describing feelings accurately, acting with awareness, judgment of inner experience, and reactivity to inner experience) predicted pre-1MFU reduction in GAD severity and pre-1MFU reduction in trait perseverative cognitions from MEMI but not SM. MEMI reduced pre–post reactivity to inner experiences (but not other mindfulness domains) significantly more than SM. Only reduced pre–post reactivity significantly mediated stronger efficacy of MEMI over SM on pre-1MFU reductions in GAD severity (indirect effect: β = −2.970 [−5.034, −0.904], p = .008; b path: β = −3.313 [−6.350, −0.276], p = .033; percentage mediated: 30.5%) and trait perseverative cognitions (indirect effect: β = −0.153 [−0.254, −0.044], p = .008; b path: β = −0.145 [−0.260, −0.030], p = .014; percentage mediated: 42.7%). Other trait mindfulness domains were non-significant mediators.
Conclusions
Reactivity to inner experience might be a mindfulness-based intervention change mechanism and should be targeted to optimize brief MEMIs for GAD.
Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples – the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.