Commentary: Incorporating concepts and methods from causal inference into life course epidemiology

The central thesis of this book is that causal inferences based on careful integrative multi-method research designs have advantages over similar inferences that rely on only one method. A necessary first step in making this argument is showing that many applications of qualitative and quantitative methods in the social sciences share an underlying notion of causation. If causation in these two methodological traditions is fundamentally not the same sort of relation, then there is good reason to worry that they cannot contribute to a joint causal project. Indeed, given such fundamentally divergent worldviews, it is unclear that the methods could even contribute to testing each others’ assumptions – the central theme of integrative multi-method research designs. I argue below that the potential-outcomes framework, in which causal effects are characterized as the difference between what actually happens in a given case and what would have happened had that case been assigned to a different treatment category, can serve as that notion. After introducing this concept, I argue that stereotypically qualitative and quantitative causal thinking can be seen as sharing an underlying conceptual structure. The potential-outcomes framework is used to explicate causal models and hypotheses throughout the book. Existing work on the potential-outcomes framework has produced clear insights into the challenges of causal inference. Most famously, authors have pointed to the fundamental problem of causal inference: because it is impossible to observe both what does happen to a case in reality and what would have happened to that case had the independent variable been assigned a different value, no direct empirical observation of causation at the case level is possible (Holland 1986). Prior research also provides detailed findings regarding the assumptions necessary to get around this problem and make causal inferences in experiments, natural experiments, and quantitative observational studies (e.g. Morgan and Winship 2007). The last segment of this chapter reviews those findings with respect to regression-type analysis of observational studies, paying special attention to the assumptions about control variables necessary for regression-based causal inference. In particular, this literature shows that scholars need independent evidence that the regression does not suffer from confounders, colliders, or post-treatment bias. While these assumptions are not sufficient for causal inference, they are necessary and provide key openings for integrative multi-method research design.

This paper reviews recent research on causal inference with large-scale assessments in education from a Bayesian perspective. I begin by adopting the potential outcomes model of Rubin (J Educ Psychol 66:688–701, 1974) as a framework for causal inference that I argue is appropriate with large-scale educational assessments. I then discuss the elements of Bayesian inference arguing that methods and models of causal inference can benefit from the Bayesian approach to quantifying uncertainty. Next I outline one method of causal inference that I believe is fruitful for addressing causal questions with large-scale educational assessments within the potential outcomes framework— namely, propensity score analysis. I then discuss the quantification of uncertainty in propensity score analysis through a Bayesian approach. Next, I discuss a series of necessary conditions for addressing causal questions with large-scale educational assessments. The paper closes with a discussion of the implications for the design of large-scale educational assessments when the goal is in asking causal questions and warranting causal claims.

The Directed Acyclic Graph (DAG) is a graph representing causal pathways for informing the conduct of an observational study. The use of DAGs allows transparent communication of a causal model between researchers and can prevent over-adjustment biases when conducting causal inference, permitting greater confidence and transparency in reported causal estimates. In the era of ‘big data’ and increasing number of observational studies, the role of the DAG is becoming more important. Recent best-practice guidance for constructing a DAG with reference to the literature has been published in the ‘Evidence synthesis for constructing DAGs’ (ESC-DAG) protocol. We aimed to assess adherence to these principles for DAGs constructed within perioperative literature. Following registration on the International Prospective Register of Systematic Reviews (PROSPERO) and with adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting framework for systematic reviews, we searched the Excerpta Medica dataBASE (Embase), the Medical Literature Analysis and Retrieval System Online (MEDLINE) and Cochrane databases for perioperative observational research incorporating a DAG. Nineteen studies were included in the final synthesis. No studies demonstrated any evidence of following the mapping stage of the protocol. Fifteen (79%) fulfilled over half of the translation and integration one stages of the protocol. Adherence with one stage did not guarantee fulfilment of the other. Two studies (11%) undertook the integration two stage. Unmeasured variables were handled inconsistently between studies. Only three (16%) studies included unmeasured variables within their DAG and acknowledged their implication within the main text. Overall, DAGs that were constructed for use in perioperative observational literature did not consistently adhere to best practice, potentially limiting the benefits of subsequent causal inference. Further work should focus on exploring reasons for this deviation and increasing methodological transparency around DAG construction.

In this article, we discuss both prospective and retrospective causal inferences, building on Neyman’s potential outcome framework. For prospective causal inference, we review criteria for confounders and surrogates to avoid the Yule–Simpson paradox and the surrogate paradox, respectively. For retrospective causal inference, we introduce the concepts of posterior causal effects given observed evidence to quantify the causes of effects. The posterior causal effects provide a unified framework for deducing both effects of causes in prospective causal inference and causes of effects in retrospective causal inference. We compare the medical diagnostic approaches based on Bayesian posterior probabilities and posterior causal effects for classification and attribution.

Quantifying the robustness of causal inferences: Sensitivity analysis for pragmatic social science

Understanding causal mechanisms among variables is critical to efficient management of complex biological systems such as animal agriculture production. The increasing availability of data from commercial livestock operations offers unique opportunities for attaining causal insight, despite the inherently observational nature of these data. Causal claims based on observational data are substantiated by recent theoretical and methodological developments in the rapidly evolving field of causal inference. Thus, the objectives of this review are as follows: 1) to introduce a unifying conceptual framework for investigating causal effects from observational data in livestock, 2) to illustrate its implementation in the context of the animal sciences, and 3) to discuss opportunities and challenges associated with this framework. Foundational to the proposed conceptual framework are graphical objects known as directed acyclic graphs (DAGs). As mathematical constructs and practical tools, DAGs encode putative structural mechanisms underlying causal models together with their probabilistic implications. The process of DAG elicitation and causal identification is central to any causal claims based on observational data. We further discuss necessary causal assumptions and associated limitations to causal inference. Last, we provide practical recommendations to facilitate implementation of causal inference from observational data in the context of the animal sciences.

The Target Trial: A Powerful Device Beyond Well-defined Interventions.

Causal inference is a permanent challenge topic in statistics, data science, and many other scientific fields.In this paper, we give an overview of statistical methods for causal inference. There are two main frameworks of causal inference: the potential outcome model and the causal network model. The potential outcome framework is used to evaluate causal effects of a known treatment or exposure variable on a given response or outcome variable. We review several commonly-used approaches in this framework for causal effect evaluation.The causal network framework is used to depict causal relationships among variables and the data generation mechanism in complex systems.We review two main approaches for structural learning: the constraint-based method and the score-based method.In the recent years, the evaluation of causal effects and the structural learning of causal networks are combined together.At the first stage, the hybrid approach learns a Markov equivalent class of causal networks from observed data;then at the second stage, it evaluates the causal effect for each causal network in the class; it also obtains a set of causal effects.The current frameworks of causal inference still have various demerits and disadvantages.We discuss these challenges and possible solutions in modern big data studies.

Based on benchmarking various methods and analyzing multiple real scRNA-seq datasets, a computational platform/workflow and a set of tips for best practices are developed for analyzing causal interactions or relationships in single cells.

Partial compliance with assigned treatment regimes is common in drug trials and calls for a causal analysis of the effect of treatment actually received. As such observed exposure is no longer randomized, selection bias must be carefully accounted for. The framework of potential outcomes allows this by defining a subject-specific treatment-free reference outcome, which may be latent and is modelled in relation to the observed (treated) data. Causal parameters enter these structural models explicitly. In this paper we review recent progress in randomization-based inference for structural mean modelling, from the additive linear model to the structural generalized linear models. An arsenal of tools currently available for standard association regression has steadily been developed in the structural setting, providing many parallel features to help randomization-based inference. We argue that measurement error on exposure is an important practical complication that has, however, not yet been addressed. We show how standard additive linear structural mean models are robust against unbiased measurement error and how efficient, asymptotically unbiased inference can be drawn when the degree of measurement error bias is known. The impact of measurement error is illustrated in a blood pressure example and finite sample properties are verified by simulation. We end with a plea for more and careful use of this methodology and point to directions for further development.

BackgroundSARS-CoV-2 strains evolve continuously and accumulate mutations in their genomes over the course of the pandemic. The severity of a SARS-CoV-2 infection could partly depend on these viral genetic characteristics. Here, we present a general conceptual framework that allows to study the effect of SARS-CoV-2 variants on COVID-19 disease severity among hospitalized patients.MethodsA causal model is defined and visualized using a Directed Acyclic Graph (DAG), in which assumptions on the relationship between (confounding) variables are made explicit. Various DAGs are presented to explore specific study design options and the risk for selection bias. Next, the data infrastructure specific to the COVID-19 surveillance in Belgium is described, along with its strengths and weaknesses for the study of clinical impact of variants.DiscussionA well-established framework that provides a complete view on COVID-19 disease severity among hospitalized patients by combining information from different sources on host factors, viral factors, and healthcare-related factors, will enable to assess the clinical impact of emerging SARS-CoV-2 variants and answer questions that will be raised in the future. The framework shows the complexity related to causal research, the corresponding data requirements, and it underlines important limitations, such as unmeasured confounders or selection bias, inherent to repurposing existing routine COVID-19 data registries.Trial registrationEach individual research project within the current conceptual framework will be prospectively registered in Open Science Framework (OSF identifier: https://doi.org/10.17605/OSF.IO/UEF29). OSF project created on 18 May 2021.

ABSTRACTBackgroundSurvey data are essential in paediatric epidemiology, providing valuable insights into child health outcomes. The potential outcomes framework has advanced causal inference using observational data. However, traditional design‐based adjustments, especially sample weights, are often overlooked. This omission limits the ability to generalise findings to the broader population.ObjectiveThis study demonstrates three approaches for estimating the population average treatment effect (PATE) in a practical example, examining the impact of household second‐hand smoke (SHS) exposure on blood pressure in school‐aged children.MethodsUsing data from the National Health and Nutrition Examination Survey (NHANES) 2017–2020, we assessed the effect of household SHS exposure, a non‐randomised treatment, on blood pressure in school‐aged children. We applied estimators based on Inverse Probability of Treatment Weighting (IPTW), G‐computation, Targeted Maximum Likelihood Estimation (TMLE), and regression adjustment. Models without adjustments were run for comparison. We examined point estimates and the efficiency of the estimates obtained from these methods.ResultsThe largest differences were observed between the unadjusted regression models and the fully adjusted methods (IPTW, G‐computation, and TMLE), which account for both confounding and survey weights. While the inclusion of the sample weights leads to wider confidence intervals for all methods, G‐computation and TMLE showed comparatively narrower confidence intervals. Confidence intervals for the models not adjusted for sample weights were likely underestimated.ConclusionsThis study highlights the important role of sample weights in causal inference. Generalisability of the average treatment effect as estimated on data sampled using common survey designs to a defined population requires the use of sample weights. The estimators described provide a framework for incorporating sample weights, and their use in health research is recommended.

Directed acyclic graphs (DAGs) play a large role in the modern approach to causal inference. DAGs describe the relationship between measurements taken at various discrete times including the effect of interventions. The causal mechanisms, on the other hand, would naturally be assumed to be a continuous process operating over time in a cause–effect fashion. How does such immediate causation, that is causation occurring over very short time intervals, relate to DAGs constructed from discrete observations? We introduce a time-continuous model and simulate discrete observations in order to judge the relationship between the DAG and the immediate causal model. We find that there is no clear relationship; indeed the Bayesian network described by the DAG may not relate to the causal model. Typically, discrete observations of a process will obscure the conditional dependencies that are represented in the underlying mechanistic model of the process. It is therefore doubtful whether DAGs are always suited to describe causal relationships unless time is explicitly considered in the model. We relate the issues to mechanistic modeling by using the concept of local (in)dependence. An example using data from the Swiss HIV Cohort Study is presented.

Adjusting for Selection Effects in Epidemiologic Studies

An increasing number of researchers in psychology are collecting intensive longitudinal data in order to study psychological processes on an intraindividual level. An increasingly popular way to analyze these data is autoregressive time series modeling; either by modeling the repeated measures for a single individual using classic n = 1 autoregressive models, or by using multilevel extensions of these models, with the dynamics for each individual modeled at Level 1 and interindividual differences in these dynamics modeled at Level 2. However, while it is widely accepted in psychology that psychological measurements usually contain a certain amount of measurement error, the issue of measurement error is largely neglected in applied psychological (autoregressive) time series modeling: The regular autoregressive model incorporates innovations, or "dynamic errors," but not measurement error. In this article we discuss the concepts of reliability and measurement error in the context of dynamic (VAR(1)) models, and the consequences of disregarding measurement error variance in the data. For this purpose, we present a preliminary model that accounts for measurement error for constructs that are measured with a single indicator. We further discuss how this model could be used to investigate the between-person reliability of the measurements, as well as the (person-specific) within-person reliabilities and any individual differences in these reliabilities. We illustrate the consequences of assuming perfect reliability, the preliminary model, and reliabilities, using an empirical application in which we relate women's general positive affect to their positive affect concerning their romantic relationship. (PsycINFO Database Record (c) 2019 APA, all rights reserved).