Confounder selection strategies targeting stable treatment effect estimators

Inferring the causal effect of a treatment on an outcome in an observational study requires adjusting for observed baseline confounders to avoid bias. However, adjusting for all observed baseline covariates, when only a subset are confounders of the effect of interest, is known to yield potentially inefficient and unstable estimators of the treatment effect. Furthermore, it raises the risk of finite-sample bias and bias due to model misspecification. For these stated reasons, confounder (or covariate) selection is commonly used to determine a subset of the available covariates that is sufficient for confounding adjustment. In this article, we propose a confounder selection strategy that focuses on stable estimation of the treatment effect. In particular, when the propensity score model already includes covariates that are sufficient to adjust for confounding, then the addition of covariates that are associated with either treatment or outcome alone, but not both, should not systematically change the effect estimator. The proposal, therefore, entails first prioritizing covariates for inclusion in the propensity score model, then using a change-in-estimate approach to select the smallest adjustment set that yields a stable effect estimate. The ability of the proposal to correctly select confounders, and to ensure valid inference of the treatment effect following data-driven covariate selection, is assessed empirically and compared with existing methods using simulation studies. We demonstrate the procedure using three different publicly available datasets commonly used for causal inference

Nonlinear effect of social interaction quantity on psychological well-being:Diminishing returns or inverted U?

Social contact is an important ingredient of a happy and satisfying life. But is more social contact necessarily better? Although it is well-established that increasing the quantity of social interactions on the low end of its spectrum promotes psychological well-being, the effect of interaction quantity on the high end remains largely unexplored. We propose that the effect of interaction quantity is nonlinear; specifically, at high levels of interaction quantity, its positive effects may be reduced (Diminishing Returns Hypothesis) or even reversed (Inverted U Hypothesis). To test these two competing hypotheses, we conducted a series of six studies involving a total of 161,836 participants using experimental (Study 1), cross-sectional (Studies 2 and 3), daily diary (Study 4), experience sampling (Study 5), and longitudinal survey designs (Study 6). Consistent evidence emerged across the studies supporting the Diminishing Returns Hypothesis. On the low end of the interaction quantity spectrum, increasing interaction quantity enhanced well-being as expected; whereas on the high end of the spectrum, the effect of interaction quantity was reduced or became nearly negligible, but did not turn negative. Taken together, the present research provides compelling evidence that the well-being benefits of social interactions are nearly negligible after moderate quantities of interactions are achieve

Disentangling indirect effects through multiple mediators whose causal structure is unknown

When there are multiple mediators on the causal pathway from a treatment to an outcome, path analysis is commonly used to disentangle the specific indirect effects transmitted along causal path(s) through each distinct mediator. However, fine-grained decompositions of specific indirect or mediated effects along separate paths are only valid under stringent assumptions, such as a correctly specified causal structure of the mediators, and no unobserved confounding of the mediators. In this article, we introduce a new type of direct and indirect effects for multiple mediators, called interventional effects, from the causal inference and epidemiology literature. While the framework for interventional direct and indirect effects can accommodate nonlinear models for the means of the mediators and/or the outcome in general, we will focus on a particular class of linear models widely used for multiple mediation analysis. We demonstrate how the interventional indirect effects through each distinct mediator can be unbiasedly estimated using prevalent path analysis methods within the linear structural equation modeling (SEM) framework. The estimators do not require specifying the directions of the causal effects between the mediators, and are unbiased (in large samples) even when the mediators share hidden or unobserved common causes. The estimation method is utilized to assess the effect of political inclusion on political prejudice that is possibly mediated by six distinct mediators

Nonlinear mediation analysis with high‐dimensional mediators whose causal structure is unknown

With multiple possible mediators on the causal pathway from a treatment to an outcome, we consider the problem of decomposing the effects along multiple possible causal path(s) through each distinct mediator. Under a path-specific effects framework, such fine-grained decompositions necessitate stringent assumptions, such as correctly specifying the causal structure among the mediators, and no unobserved confounding among the mediators. In contrast, interventional direct and indirect effects for multiple mediators can be identified under much weaker conditions, while providing scientifically relevant causal interpretations. Nonetheless, current estimation approaches require (correctly) specifying a model for the joint mediator distribution, which can be difficult when there is a high-dimensional set of possibly continuous and noncontinuous mediators. In this article, we avoid the need to model this distribution, by developing a definition of interventional effects previously suggested for longitudinal mediation. We propose a novel estimation strategy that uses nonparametric estimates of the (counterfactual) mediator distributions. Noncontinuous outcomes can be accommodated using nonlinear outcome models. Estimation proceeds via Monte Carlo integration. The procedure is illustrated using publicly available genomic data to assess the causal effect of a microRNA expression on the 3-month mortality of brain cancer patients that is potentially mediated by expression values of multiple genes

Nonlinear mediation analysis with high-dimensional mediators whose causal structure is unknown.

With multiple possible mediators on the causal pathway from a treatment to an outcome, we consider the problem of decomposing the effects along multiple possible causal path(s) through each distinct mediator. Under a path-specific effects framework, such fine-grained decompositions necessitate stringent assumptions, such as correctly specifying the causal structure among the mediators, and no unobserved confounding among the mediators. In contrast, interventional direct and indirect effects for multiple mediators can be identified under much weaker conditions, while providing scientifically relevant causal interpretations. Nonetheless, current estimation approaches require (correctly) specifying a model for the joint mediator distribution, which can be difficult when there is a high-dimensional set of possibly continuous and noncontinuous mediators. In this article, we avoid the need to model this distribution, by developing a definition of interventional effects previously suggested for longitudinal mediation. We propose a novel estimation strategy that uses nonparametric estimates of the (counterfactual) mediator distributions. Noncontinuous outcomes can be accommodated using nonlinear outcome models. Estimation proceeds via Monte Carlo integration. The procedure is illustrated using publicly available genomic data to assess the causal effect of a microRNA expression on the 3-month mortality of brain cancer patients that is potentially mediated by expression values of multiple genes

Heterogeneous Indirect Effects for Multiple Mediators using Interventional Effect Models

Decomposing an exposure effect on an outcome into separate natural indirect effects through multiple mediators requires strict assumptions, such as correctly postulating the causal structure of the mediators, and no unmeasured confounding among the mediators. In contrast, interventional indirect effects for multiple mediators can be identified even when - as often - the mediators either have an unknown causal structure, or share unmeasured common causes, or both. Existing estimation methods for interventional indirect effects require calculating each distinct indirect effect in turn. This can quickly become unwieldy or unfeasible, especially when investigating indirect effect measures that may be modified by observed baseline characteristics. In this article, we introduce simplified estimation procedures for such heterogeneous interventional indirect effects using interventional effect models. Interventional effect models are a class of marginal structural models that encode the interventional indirect effects as causal model parameters, thus readily permitting effect modification by baseline covariates using (statistical) interaction terms. The mediators and outcome can be continuous or noncontinuous. We propose two estimation procedures: one using inverse weighting by the counterfactual mediator density or mass functions, and another using Monte Carlo integration. The former has the advantage of not requiring an outcome model, but is susceptible to finite sample biases due to highly variable weights. The latter has the advantage of consistent estimation under a correctly specified (parametric) outcome model, but is susceptible to biases due to extrapolation

Deep Learning the Effects of Photon Sensors on the Event Reconstruction Performance in an Antineutrino Detector

We provide a fast approach incorporating the usage of deep learning for evaluating the effects of photon sensors in an antineutrino detector on the event reconstruction performance therein. This work is an attempt to harness the power of deep learning for detector designing and upgrade planning. Using the Daya Bay detector as a benchmark case and the vertex reconstruction performance as the objective for the deep neural network, we find that the photomultiplier tubes (PMTs) have different relative importance to the vertex reconstruction. More importantly, the vertex position resolutions for the Daya Bay detector follow approximately a multi-exponential relationship with respect to the number of PMTs and hence, the coverage. This could also assist in deciding on the merits of installing additional PMTs for future detector plans. The approach could easily be used with other objectives in place of vertex reconstruction