arXiv subjects

Marco Piccininni

Publications and source records attributed to Marco Piccininni.

6 recordsLinked to original sources

arXiv2026

Causal Inference from Self-controlled Case Series

The self-controlled case series (SCCS) design is widely used in studies of rare events, for example of vaccine side effects. Conceptually, the design has two main features. First, it requires only data from individuals who have experienced the event of interest. Therefore, the design is especially useful when the outcome is rare and the available data come from surveillance or clinical records, say, of hospitalized patients. Second, because comparisons are made within single individuals, time-invariant confounding between treatment and outcome is eliminated. We formulate a set of sufficient identification assumptions for estimating the causal effect of vaccination on an adverse outcome under the SCCS design, drawing on ideas from the causal inference literature. Unlike the existing SCCS literature, we do not consider a parametric model as the starting point. This allows us to establish when and why the SCCS design allows identification of causal effects, using data from cases only. However, because the sufficient identifiability assumptions are implausible in some applications, we also develop statistical tests for the existence of a treatment effect that remain valid when only a subset of the identification assumptions holds.

stat.ME↗

arXiv2026

Refining the Notion of No Anticipation in Difference-in-Differences Studies

We clarify the interpretation of identification strategies using difference-in-differences (DiD), which are widely applied in empirical research. In particular, DiD researchers often invoke an assumption commonly referred to as the "no-anticipation assumption". Taken literally, this assumption states that treatments in the future have no effect on outcomes in the past. Because standard causal models rely on a temporal structure in which causes precede effects, such an assumption seems to be inherently satisfied. However, most researchers attribute a different meaning to the assumption: they interpret the assumption as ensuring no effect of policy decision, e.g., immediate behavioral changes due to knowledge about the plan to implement the policy. We discuss the consequences of this mismatch, and give an alternative formulation of no anticipation that captures DiD researchers' concerns and clarifies identification arguments.

stat.ME↗

arXiv2026

Causal inference for N-of-1 trials

The aim of personalized medicine is to tailor treatment decisions to individuals' characteristics. N-of-1 trials are within-person crossover trials that hold the promise of targeting individual-specific effects. While the idea behind N-of-1 trials might seem simple, analyzing and interpreting N-of-1 trials is not straightforward. Here we ground N-of-1 trials in a formal causal inference framework and formalize intuitive claims from the N-of-1 trials literature. We focus on causal inference from a single N-of-1 trial and define a conditional average treatment effect (CATE) that represents a target in this setting, which we call the U-CATE. We discuss assumptions sufficient for identification and estimation of the U-CATE under different causal models where the treatment schedule is assigned at baseline. A simple mean difference is an unbiased, asymptotically normal estimator of the U-CATE in simple settings. We also consider settings where carryover effects, trends over time, time-varying common causes of the outcome, and outcome-outcome effects are present. In these more complex settings, we show that a time-varying g-formula identifies the U-CATE under explicit assumptions. Finally, we analyze data from N-of-1 trials about acne symptoms and show how different assumptions about the data generating process can lead to different analytical strategies.

stat.ME↗

arXiv2026

Effects conditional on post-treatment events generated by independent mechanisms

In both observational studies and randomized trials, post-treatment events such as dropout, nonadherence, and truncation by death occur frequently. In some studies, conditioning on post-treatment variables is a deliberate strategy to isolate particular treatment effects on the outcome. However, naive comparisons of outcomes conditional on post-treatment events generally lack a causal interpretation, even when treatment is randomly assigned. There exist causal estimands that account for post-treatment events, including survivor average causal effects and conditional separable effects, but identification usually requires measurement of common causes of the post-treatment event and the outcome. In this article, we show that such measurements are not always necessary. Conceptually, what we require is that the treatment and other unmeasured causes of the outcome generate the post-treatment event through "independent mechanisms". Then, conditional separable effects and survivor average causal effects can be identified without adjustment for common causes of the post-treatment event and the outcome. We illustrate the results in studies with truncating events, differential nonadherence, and the birth weight paradox.

stat.ME↗

arXiv2026

On the choice of using raw or demographically-corrected scores

Demographic corrections are routinely performed in many disciplines, including psychology. Yet, there are ongoing debates about whether these corrections are appropriate and improve classification accuracy. Here, we focus on cognitive screening tests, and show that common demographic corrections, like the z-score correction, can be detrimental for classification in some settings. Formally, we present sufficient conditions ensuring that raw scores outperform the demographically-corrected ones, and give a substantive interpretation of this result. We also investigate the claim that using demographically-corrected scores results in more fair decisions compared to using raw scores. We apply our results to the Mini-Mental State Examination in the OASIS-3 dataset.

stat.ME↗

arXiv2020

Directed Acyclic Graphs and causal thinking in clinical risk prediction modeling

Background: In epidemiology, causal inference and prediction modeling methodologies have been historically distinct. Directed Acyclic Graphs (DAGs) are used to model a priori causal assumptions and inform variable selection strategies for causal questions. Although tools originally designed for prediction are finding applications in causal inference, the counterpart has remained largely unexplored. The aim of this theoretical and simulation-based study is to assess the potential benefit of using DAGs in clinical risk prediction modeling. Methods and Findings: We explore how incorporating knowledge about the underlying causal structure can provide insights about the transportability of diagnostic clinical risk prediction models to different settings. A single-predictor model in the causal direction is likely to have better transportability than one in the anticausal direction. We further probe whether causal knowledge can be used to improve predictor selection. We empirically show that the Markov Blanket, the set of variables including the parents, children, and parents of the children of the outcome node in a DAG, is the optimal set of predictors for that outcome. Conclusions: Our findings challenge the generally accepted notion that a change in the distribution of the predictors does not affect diagnostic clinical risk prediction model calibration if the predictors are properly included in the model. Furthermore, using DAGs to identify Markov Blanket variables may be a useful, efficient strategy to select predictors in clinical risk prediction models if strong knowledge of the underlying causal structure exists or can be learned.

stat.ME↗