Robust and Flexible Estimation of Stochastic Mediation Effects: A Proposed Method and Example in a Randomized Trial Setting

Causal mediation analysis can improve understanding of the mechanisms underlying epidemiologic associations. However, the utility of natural direct and indirect effect estimation has been limited by the assumption of no confounder of the mediator-outcome relationship that is affected by prior exposure---an assumption frequently violated in practice. We build on recent work that identified alternative estimands that do not require this assumption and propose a flexible and double robust semiparametric targeted minimum loss-based estimator for data-dependent stochastic direct and indirect effects. The proposed method treats the intermediate confounder affected by prior exposure as a time-varying confounder and intervenes stochastically on the mediator using a distribution which conditions on baseline covariates and marginalizes over the intermediate confounder. In addition, we assume the stochastic intervention is given, conditional on observed data, which results in a simpler estimator and weaker identification assumptions. We demonstrate the estimator's finite sample and robustness properties in a simple simulation study. We apply the method to an example from the Moving to Opportunity experiment. In this application, randomization to receive a housing voucher is the treatment/instrument that influenced moving to a low-poverty neighborhood, which is the intermediate confounder. We estimate the data-dependent stochastic direct effect of randomization to the voucher group on adolescent marijuana use not mediated by change in school district and the stochastic indirect effect mediated by change in school district. We find no evidence of mediation. Our estimator is easy to implement in standard statistical software, and we provide annotated R code to further lower implementation barriers.Comment: 24 pages, 2 tables, 2 figure

Causal Induction from Continuous Event Streams: Evidence for Delay-Induced Attribution Shifts

Contemporary theories of Human Causal Induction assume that causal knowledge is inferred from observable contingencies. While this assumption is well supported by empirical results, it fails to consider an important problem-solving aspect of causal induction in real time: In the absence of well structured learning trials, it is not clear whether the effect of interest occurred because of the cause under investigation, or on its own accord. Attributing the effect to either the cause of interest or alternative background causes is an important precursor to induction. We present a new paradigm based on the presentation of continuous event streams, and use it to test the Attribution-Shift Hypothesis (Shanks & Dickinson, 1987), according to which temporal delays sever the attributional link between cause and effect. Delays generally impaired attribution to the candidate, and increased attribution to the constant background of alternative causes. In line with earlier research (Buehner & May, 2002, 2003, 2004) prior knowledge and experience mediated this effect. Pre-exposure to a causally ineffective background context was found to facilitate the discovery of delayed causal relationships by reducing the tendency for attributional shifts to occur. However, longer exposure to a delayed causal relationship did not improve discovery. This complex pattern of results is problematic for associative learning theories, but supports the Attribution-Shift Hypothesi

A Primer on Causality in Data Science

Many questions in Data Science are fundamentally causal in that our objective is to learn the effect of some exposure, randomized or not, on an outcome interest. Even studies that are seemingly non-causal, such as those with the goal of prediction or prevalence estimation, have causal elements, including differential censoring or measurement. As a result, we, as Data Scientists, need to consider the underlying causal mechanisms that gave rise to the data, rather than simply the pattern or association observed in those data. In this work, we review the 'Causal Roadmap' of Petersen and van der Laan (2014) to provide an introduction to some key concepts in causal inference. Similar to other causal frameworks, the steps of the Roadmap include clearly stating the scientific question, defining of the causal model, translating the scientific question into a causal parameter, assessing the assumptions needed to express the causal parameter as a statistical estimand, implementation of statistical estimators including parametric and semi-parametric methods, and interpretation of our findings. We believe that using such a framework in Data Science will help to ensure that our statistical analyses are guided by the scientific question driving our research, while avoiding over-interpreting our results. We focus on the effect of an exposure occurring at a single time point and highlight the use of targeted maximum likelihood estimation (TMLE) with Super Learner.Comment: 26 pages (with references); 4 figure

A mediation approach to understanding socio-economic inequalities in maternal health-seeking behaviours in Egypt.

BACKGROUND: The levels and origins of socio-economic inequalities in health-seeking behaviours in Egypt are poorly understood. This paper assesses the levels of health-seeking behaviours related to maternal care (antenatal care [ANC] and facility delivery) and their accumulation during pregnancy and childbirth. Secondly, it explores the mechanisms underlying the association between socio-economic position (SEP) and maternal health-seeking behaviours. Thirdly, it examines the effectiveness of targeting of free public ANC and delivery care. METHODS: Data from the 2008 Demographic and Health Survey were used to capture two latent constructs of SEP: individual socio-cultural capital and household-level economic capital. These variables were entered into an adjusted mediation model, predicting twelve dimensions of maternal health-seeking; including any ANC, private ANC, first ANC visit in first trimester, regular ANC (four or more visits during pregnancy), facility delivery, and private delivery. ANC and delivery care costs were examined separately by provider type (public or private). RESULTS: While 74.2% of women with a birth in the 5-year recall period obtained any ANC and 72.4% delivered in a facility, only 48.8% obtained the complete maternal care package (timely and regular facility-based ANC as well as facility delivery) for their most recent live birth. Both socio-cultural capital and economic capital were independently positively associated with receiving any ANC and delivering in a facility. The strongest direct effect of socio-cultural capital was seen in models predicting private provider use of both ANC and delivery. Despite substantial proportions of women using public providers reporting receipt of free care (ANC: 38%, delivery: 24%), this free-of-charge public care was not effectively targeted to women with lowest economic resources. CONCLUSIONS: Socio-cultural capital is the primary mechanism leading to inequalities in maternal health-seeking in Egypt. Future studies should therefore examine the objective and perceived quality of care from different types of providers. Improvements in the targeting of free public care could help reduce the existing SEP-based inequalities in maternal care coverage in the short term

Apperceptive patterning: Artefaction, extensional beliefs and cognitive scaffolding

In “Psychopower and Ordinary Madness” my ambition, as it relates to Bernard Stiegler’s recent literature, was twofold: 1) critiquing Stiegler’s work on exosomatization and artefactual posthumanism—or, more specifically, nonhumanism—to problematize approaches to media archaeology that rely upon technical exteriorization; 2) challenging how Stiegler engages with Giuseppe Longo and Francis Bailly’s conception of negative entropy. These efforts were directed by a prevalent techno-cultural qualifier: the rise of Synthetic Intelligence (including neural nets, deep learning, predictive processing and Bayesian models of cognition). This paper continues this project but first directs a critical analytic lens at the Derridean practice of the ontologization of grammatization from which Stiegler emerges while also distinguishing how metalanguages operate in relation to object-oriented environmental interaction by way of inferentialism. Stalking continental (Kapp, Simondon, Leroi-Gourhan, etc.) and analytic traditions (e.g., Carnap, Chalmers, Clark, Sutton, Novaes, etc.), we move from artefacts to AI and Predictive Processing so as to link theories related to technicity with philosophy of mind. Simultaneously drawing forth Robert Brandom’s conceptualization of the roles that commitments play in retrospectively reconstructing the social experiences that lead to our endorsement(s) of norms, we compliment this account with Reza Negarestani’s deprivatized account of intelligence while analyzing the equipollent role between language and media (both digital and analog)

Methodological developments in constructing casual diagrams with counterfactual analysis of adolescent alcohol harm

Background and aims: Causal diagrams, or Directed Acyclic Graphs (DAGs), are mathematically formulated networks of nodes (variables) and arrows which rigorously identify adjustment sets for statistical models. They are thus promising tools for improving statistical analysis in health and social sciences. However, a lack of pragmatic yet robust guidance for building DAGs has been identified as problematic for their use in applied research. This thesis aims to contribute an example of such guidance in the form of a novel research method, and to demonstrate this method’s utility by applying it to observational data. Design: This thesis introduces ‘Evidence Synthesis for Constructing Directed Acyclic Graphs’ (ESC-DAGs) as a protocol for building DAGs from research evidence. It is demonstrated here in the context of parental influences on adolescent alcohol harm and the resulting DAGs are used to inform analysis of data from the Avon Longitudinal Study of Parents and Children (ALSPAC). Methods: ESC-DAGs integrates evidence synthesis principles with classic and modern perspectives on causal inference to produce complex DAGs in a systematic and transparent way. It was applied here to a subset of literature identified from a novel review of systematic reviews, which identified 12 parental influences on adolescent alcohol harm. ESC-DAGs was then further applied to the ALSPAC data to produce a ‘data integrated DAG’. The outcome measure was the Alcohol Use Disorders Identification Test (AUDIT) administered to adolescent participants at age 16.5 years. Nine parental influences were measured, alongside 22 intermediates (variables lying on the causal pathway between parental influences and AUDIT score). The DAGs were then used to direct two stages of analysis: 1) weighting and regression techniques were used to estimate Average Causal Effects (ACEs) for each parental influence and intermediate; and 2) causal mediation analysis was used to decompose the effect of maternal drinking on adolescent AUDIT score to estimate Natural Indirect Effects (NIEs) for the intermediates and the other parental influences. Findings: Evidence for an ACE was found for each parental influence. Parental drinking, low parental monitoring, and parental permissiveness towards adolescent alcohol use had larger effects that were more robust to sensitivity analysis. Several peer and intrapersonal intermediates had higher effects. There was little evidence of an NIE of maternal drinking through other parental influences. There were substantial NIEs for substance-related behaviours of the adolescent and their peers. Conclusions: ESC-DAGs is a promising tool for using DAGs to improve statistical practices. The DAGs produced were transparent and able to direct various forms of data analysis in an immediate sense while differentiating between a comparatively large volume of confounders and other covariates. Future development is possible and should focus on efficiency, replicability, and integration with other methods, such as risk of bias tools. ESC-DAGs may thus prove a valuable platform for discussion in the DAG and wider quantitative research communities. The statistical analyses were performed with methods that were novel to the literature and findings triangulated with the wider evidence base. Mediation analysis provided novel evidence on how parental drinking influences adolescent alcohol harm

A birth cohort study

Funding Information: The MINA‐Brazil Study has been funded by the Brazilian National Council for Scientific and Technological Development (CNPq, grant number 407255/2013‐3) and the São Paulo Research Foundation (FAPESP, grant number 2016/00270‐6). Dr Matijasevich, Dr Antunes and Dr Cardoso are recipients of CNPq senior research scholarships. Mrs Maruyama and Mrs Pinheiro received doctoral scholarship from FAPESP (grant 2017/22723‐5) and CAPES, respectively. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. Publisher Copyright: © 2022 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.Objectives: Previous cohort studies have found a positive association between prolonged breastfeeding (≥12 months) on dental caries, but few of them analysed the mediated effect of sugar consumption on this association. This study investigated whether prolonged breastfeeding is a risk factor for caries at 2-year follow-up assessment (21–27 months of age) and whether this effect is mediated by sugar consumption. Methods: A birth cohort study was performed in the Brazilian Amazon (n = 800). Dental caries was assessed using the dmf-t index. Prolonged breastfeeding was the main exposure. Data on baseline covariables and sugar consumption at follow-up visits were analysed. We estimated the OR for total causal effect (TCE) and natural indirect effect (NIE) of prolonged breastfeeding on dental caries using the G-formula. Results: The prevalence of caries was 22.8% (95% CI: 19.8%–25.8%). Children who were breastfed for 12–23 months (TCE = 1.13, 95% CI: 1.05–1.20) and for ≥24 months (TCE = 1.27, 95% CI: 1.14–1.40) presented a higher risk of caries at age of 2 years than those breastfed <12 months. However, this risk was slightly mediated by a decreased frequency of sugar consumption at age of 2 years only for breastfeeding from 12 to 23 months (NIE; OR = 0.95, 95% CI: 0.91–0.97). Conclusions: In this study, the effect of prolonged breastfeeding on the increased risk of dental caries was slightly mediated by sugar consumption. Early feeding practices for caries prevention and promoting breastfeeding while avoiding sugar consumption should be targeted in the first 2 years of life.publishersversioninpres

Identification And Robust Estimation Of Swapped Direct And Indirect Effects: Mediation Analysis With Unmeasured Mediator–Outcome Confounding And Intermediate Confounding

Counterfactual-model-based mediation analysis can yield substantial insight into the causal mechanism through the assessment of natural direct effects (NDEs) and natural indirect effects (NIEs). However, the assumptions regarding unmeasured mediator–outcome confounding and intermediate mediator–outcome confounding that are required for the determination of NDEs and NIEs present practical challenges. To address this problem, we introduce an instrumental blocker, a novel quasi-instrumental variable, to relax both of these assumptions, and we define a swapped direct effect (SDE) and a swapped indirect effect (SIE) to assess the mediation. We show that the SDE and SIE are identical to the NDE and NIE, respectively, based on a causal interpretation. Moreover, the empirical expressions of the SDE and SIE are derived with and without an intermediate mediator–outcome confounder. Then, a bias formula is developed to examine the plausibility of the proposed instrumental blocker. Moreover, a multiply robust estimation method is derived to mitigate the model misspecification problem. We prove that the proposed estimator is consistent, asymptotically normal, and achieves the semiparametric efficiency bound. As an illustration, we apply the proposed method to genomic datasets of lung cancer to investigate the potential role of the epidermal growth factor receptor in the treatment of lung cancer