Individual patient data meta-analyses (IPDMAs) provide powerful tools for synthesizing evidence across studies, yet methods for addressing unmeasured confounding in observational IPDMAs with survival outcomes are rarely implemented. Instrumental variable (IV) approaches offer causal inference capabilities but face practical challenges in hierarchical data structures, particularly the lack of standard diagnostics for instrument strength in nonlinear mixed-effects models. We adapt and evaluate a frequentist mixed-effects two-stage residual inclusion (2SRI) framework for survival IPDMAs, extending traditional IV methods to accommodate study-level and temporal clustering while handling time-to-event outcomes through Cox proportional hazards models. Because classical F-statistics are unavailable for logistic mixed-effects first-stage models, we propose the Wald $\chi ^2$ statistic as a practical instrument-strength diagnostic and empirically characterize its relationship to estimator performance. Through a comprehensive simulation study with 48 scenarios—varying unmeasured confounding (weak to very strong), instrument–treatment association strength (0.3–1.0), and cross-study IV allocation patterns—we evaluated 2SRI against naive mixed-effects Cox models using bias, coverage, variance, and mean squared error. The design was anchored to realistic IPDMA structure (10 studies, $N \approx 4,357$) from pooled Ebola data, with 1,000 replications per scenario. Results show that under weak confounding, naive models dominate on all metrics. With moderate-to-strong confounding and realized Wald $\chi ^2$ exceeding 150–200, mixed-effects 2SRI substantially reduces bias and achieves near-nominal coverage, though with inflated variance. We provide empirical guideposts linking realized first-stage strength to expected performance, enabling analysts to judge when 2SRI will outperform conventional approaches in hierarchical survival IPDMAs. All simulations assume a common treatment effect across studies. Performance under heterogeneous effects remains to be established.

The design-based paradigm may be adopted in causal inference and survey sampling when we assume Rubin’s stable unit treatment value assumption (SUTVA) or impose similar frameworks. While often taken for granted, such assumptions entail strong claims about the data-generating process. We develop an alternative design-based approach: we first invoke a generalized, non-parametric model that allows for unrestricted forms of interference, such as spillover. We define an associated set of inferential targets and discuss their interpretation under SUTVA and a weaker assumption that we call the “no unmodeled revealable variation assumption” (NURVA). We then reconstruct the standard paradigm, reconsidering SUTVA at the end rather than assuming it at the beginning. Despite its similarity to SUTVA, we demonstrate the practical limitations of NURVA alone for identifying substantively interesting quantities. In so doing, we provide clarity on the nature and importance of SUTVA for applied research.

Synthetic control methods are widely used for causal inference in case studies and panel data settings, often applied to model counterfactuals for proportional outcomes. However, conventional synthetic control methods are designed for univariate outcomes, leading researchers to model counterfactuals for each proportion separately. We make the case for jointly estimating synthetic controls across multiple compositional outcomes. Using the same weights for each proportion establishes a constant control comparison, improving comparability while adhering to compositional constraints on treatment effects. We illustrate the benefits of the method through a simulation and two applications to recent empirical studies. This implementation integrates naturally with a wide range of synthetic control approaches, providing interpretable estimates for compositional panel data common in political science.

How can we measure the effects of campaign events? We estimate how voters respond to a prominent campaign scandal—Donald Trump being convicted of 34 felony counts of falsifying business records—using data from a large, eight-wave panel study with waves fielded before, during, and after the verdict. We find the trial had virtually no effect on Trump supporters, even those who reported their support was conditional on an acquittal. We compare this precisely estimated null to estimates from popular cross-sectional methods, which fail to replicate it. The “change” question format estimates a 6% increase in support, while the “counterfactual” survey experimental design estimates a 10% decrease. We formalize the estimands each method estimates and discuss implications for the event study literature.

Many inferential tasks involve fitting models to observed data and predicting outcomes at new covariate values, requiring interpolation or extrapolation. Conventional methods select a single best-fitting model, discarding fits that were similarly plausible in-sample but would yield sharply different predictions out-of-sample. Gaussian processes (GPs) offer a principled alternative. Rather than committing to one conditional expectation function, GPs deliver a posterior distribution over outcomes at any covariate value. This posterior effectively retains the range of models consistent with the data, widening uncertainty intervals where extrapolation magnifies divergence. In this way, the GP’s uncertainty estimates reflect the implications of extrapolation on our predictions, helping to tame the “dangers of extreme counterfactuals” (King and Zeng, 2006). The approach requires (i) specifying a covariance function linking outcome similarity to covariate similarity and (ii) assuming Gaussian noise around the conditional expectation. We provide an accessible introduction to GPs with emphasis on this property, along with a simple, automated procedure for hyperparameter selection implemented in the R package gpss. We illustrate the value of GPs for capturing counterfactual uncertainty in three settings: (i) treatment effect estimation with poor overlap, (ii) interrupted time series requiring extrapolation beyond pre-intervention data, and (iii) regression discontinuity designs where estimates hinge on boundary behavior.

Dysregulation of fatty acids metabolism has been associated with the risk of osteoarthritis (OA), yet current evidence from epidemiological or genetic studies remains inconclusive. We aimed to investigate the phenotypic association and genetic architecture between total fatty acids, saturated fatty acids (SFA), MUFA, PUFA and OA. Leveraging individual-level data from the UK Biobank, combined with the hitherto largest genome-wide association studies of fatty acids (n 136 016) and OA (n 826 690) in European individuals, we implemented a comprehensive analytical framework. This included observational and genetic analyses, incorporating phenotypic associations, genetic correlations, cross-trait meta-analysis, enrichment analysis and Mendelian randomisation (MR). Observational analysis identified SFA as a risk factor, while MUFA and PUFA as protective factors for OA. Despite a lack of genome-wide genetic correlation, statistically significant local signals were detected within three specific genomic regions. Cross-trait meta-analysis identified sixty-eight pleiotropic loci shared between fatty acids and OA, of which nine were novel. Enrichment analysis revealed the shared genes were enriched in lipoprotein metabolism, immune response and inflammation regulation pathways. Two-sample MR provided evidence for a causal relationship of MUFA and PUFA on OA that survived false discovery rate correction. This study supports associations between circulating fatty acids and OA, with MUFA and PUFA exerting a protective role. Our findings provide new perspectives into OA prevention especially regarding the potential dietary interventions.

Social scientists often compare survey responses before and after important events to test how those events impact respondent beliefs, attitudes, and preferences. This article offers a formal analysis of such pre-event/post-event survey comparisons, including designs that seek to reduce bias using quota sampling, rolling cross-sections, and panels. Our analysis distinguishes major sources of bias and clarifies the comparative strengths and weaknesses of each approach. We then introduce a modified panel design—the dual randomized survey—to reduce bias in cases where asking respondents to complete the same survey twice could impact their Wave 2 responses. Our formalization of bias and novel research design improve scholars’ ability to study the causal impact of events through surveys.

The evaluation of the role of face masks in preventing respiratory infections is a paradigm case in synthesising complex evidence (i.e. extensive, diverse, technically specialised, and with multilevel chains of causality). Primary studies have assessed different mask types, diseases, populations, and settings using different research designs. Numerous review teams have attempted to synthesise this literature, in which observational (case–control, cohort, cross-sectional) and ecological studies predominate. Their findings and conclusions vary widely.

This article critically examines how 66 systematic reviews dealt with mask efficacy studies. Risk-of-bias tools produced unreliable assessments when—as was often the case—review teams lacked methodological expertise or topic-specific understanding. This was especially true when datasets were large and heterogeneous, with multiple biases playing out in different ways and requiring nuanced adjustments. In such circumstances, tools were sometimes used crudely and reductively rather than to support close reading of primary studies and guide expert judgments. Various moves by reviewers—excluding observational evidence altogether, assessing risk but not direction of biases, omitting distinguishing details of primary studies, and producing meta-analyses that combined studies of different designs or included studies at critical risk of bias—served to obscure important aspects of heterogeneity, resulting in bland and unhelpful summary statements.

We draw on philosophy to question the formulaic use of generic risk-of-bias tools, especially when the primary evidence demands expert understanding and tailoring of study quality questions to the topic. We call for more rigorous training and oversight of reviewers of complex evidence and for new review methods designed specifically for such evidence.

Over the past two decades, there has been growing interest in analyzing the effects of educational programs on outcomes using process data from computer-based testing and learning environments. However, most analyses focus on final outcomes at the end of a test or session, overlooking their functional nature over time and neglecting causal mechanisms. To address this gap, this article proposes a novel causal mediation framework for identifying and estimating functional natural direct effects, functional natural indirect effects, and functional total effects, along with their subgroup effects. We define these effects using potential outcomes and provide nonparametric identification strategies depending on whether post-treatment covariates are present or not. We then develop estimation methods using generalized additive models, a flexible and robust tool for analyzing functional data. Through a simulation study, we assess the finite-sample performance of the proposed approach by comparing it to parametric regression methods. We also demonstrate our approach by examining the effects of extended time accommodations on two functional outcomes using process data from the National Assessment of Educational Progress. Our mediation approach with functional outcomes effectively captures dynamic causal mechanisms underlying the program’s effects and pinpoints when and for whom each effect manifests throughout the testing period.

Researchers frequently deliver treatments through messages, as in many audit and get-out-the-vote (GOTV) experiments. These message-based experiments often hinge on intermediary variables—actions subjects must take to actually receive the treatment or control embedded in a message. Whether subjects open the message is a crucial intermediary step, which can serve as a condition for estimating downstream treatment effects or as an outcome of interest in its own right. Yet opens are often measured with error, most notably when some openers are misclassified as non-openers in email-based studies. We characterize the resulting bias, derive interpretable bounds on effects for well-defined subgroups, and provide sensitivity analyses for mismeasurement, thereby offering practical guidance for message-based experiments conducted through email and other communication technologies.

The question of whether and how federalism influences a country's welfare state has been a longstanding concern of political scientists. However, no agreement exists on exactly how, and under what conditions, federal structures impact the welfare state. This article examines this controversy. It concludes theoretically that the specific constellation of federal structures and distribution of powers need to be considered when theorising the effects of federalism on the welfare state. Using the case of Belgium and applying the synthetic control method, it is shown in the article that without the federalism reform of 1993, the country would have had further decreases in social spending rather than a consolidation of this spending in the years after 1993. In the case of Belgium, the combination of increased subnational spending autonomy in a still national financing system provided ideal conditions for a positive federalism effect on social spending to occur.