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We demonstrate how Hahn et al.'s Bayesian Causal Forests model (BCF) can be used to estimate conditional average treatment effects for the longitudinal dataset in the 2022 American Causal Inference Conference Data Challenge. Unfortunately, existing implementations of BCF do not scale to the size of the challenge data. Therefore, we developed flexBCF -- a more scalable and flexible implementation of BCF -- and used it in our challenge submission. We investigate the sensitivity of our results to the choice of propensity score estimation method and the use of sparsity-inducing regression tree priors. While we found that our overall point predictions were not especially sensitive to these modeling choices, we did observe that running BCF with flexibly estimated propensity scores often yielded better-calibrated uncertainty intervals.

We introduce a three-step framework to determine, on a per-pitch basis, whether batters in Major League Baseball should swing at a pitch. Unlike traditional plate discipline metrics, which implicitly assume that all batters should always swing (resp. take) pitches inside (resp. outside) the strike zone, our approach explicitly accounts not only for the players and umpires involved but also in-game contextual information like the number of outs, the count, baserunners, and score. Specifically, we first fit flexible Bayesian nonparametric models to estimate (i) the probability that the pitch is called a strike if the batter takes the pitch; (ii) the probability that the batter makes contact if he swings; and (iii) the number of runs the batting team is expected to score following each pitch outcome (e.g. swing and miss, take a called strike, etc.). We then combine these intermediate estimates to determine whether swinging increases the batting team's run expectancy. Our approach enables natural uncertainty propagation so that we can not only determine the optimal swing/take decision but also quantify our confidence in that decision. We illustrate our framework using a case study of pitches faced by Mike Trout in 2019.

The Gaussian chain graph model simultaneously parametrizes (i) the direct effects of $p$ predictors on $q$ correlated outcomes and (ii) the residual partial covariance between pair of outcomes. We introduce a new method for fitting sparse Gaussian chain graph models with spike-and-slab LASSO (SSL) priors. We develop an Expectation-Conditional Maximization algorithm to obtain sparse estimates of the $p \times q$ matrix of direct effects and the $q \times q$ residual precision matrix. Our algorithm iteratively solves a sequence of penalized maximum likelihood problems with self-adaptive penalties that gradually filter out negligible regression coefficients and partial covariances. Because it adaptively penalizes model parameters, our method is seen to outperform fixed-penalty competitors on simulated data. We establish the posterior concentration rate for our model, buttressing our method's excellent empirical performance with strong theoretical guarantees. We use our method to reanalyze a dataset from a study of the effects of diet and residence type on the composition of the gut microbiome of elderly adults.

We study the asymptotic properties of the multivariate spike-and-slab LASSO (mSSL) proposed by Deshpande et al.(2019) for simultaneous variable and covariance selection. Specifically, we consider the sparse multivariate linear regression problem where $q$ correlated responses are regressed onto $p$ covariates. In this problem, the goal is to estimate a sparse matrix $B$ of marginal covariate effects and a sparse precision matrix $\Omega$, which captures the residual conditional dependence structure of the outcomes. The mSSL works by placing continuous spike and slab priors on all the entries of $B$ and on all the off-diagonal elements in the lower-triangle of $\Omega$. Under mild assumptions, we establish the posterior contraction rate for the slightly modified mSSL posterior in the asymptotic regime where both $p$ and $q$ diverge with $n.$ Our results imply that a slightly modified version of Deshpande et al.~(2019)'s mSSL procedure is asymptotically consistent., Comment: arXiv admin note: text overlap with arXiv:2207.07020

We will study the impact of adolescent sports participation on early-adulthood health using longitudinal data from the National Study of Youth and Religion. We focus on two primary outcomes measured at ages 23--28 -- self-rated health and total score on the PHQ9 Patient Depression Questionnaire -- and control for several potential confounders related to demographics and family socioeconomic status. Comparing outcomes between sports participants and matched non-sports participants with similar confounders is straightforward. Unfortunately, an analysis based on such a broad exposure cannot probe the possibility that participation in certain types of sports (e.g. collision sports like football or soccer) may have larger effects on health than others. In this study, we introduce a hierarchy of exposure definitions, ranging from broad (participation in any after-school organized activity) to narrow (e.g. participation in limited-contact sports). We will perform separate matched observational studies, one for each definition, to estimate the health effects of several levels of sports participation. In order to conduct these studies while maintaining a fixed family-wise error rate, we developed an ordered testing approach that exploits the logical relationships between exposure definitions. Our study will also consider several secondary outcomes including body mass index, life satisfaction, and problematic drinking behavior.

Most implementations of Bayesian additive regression trees (BART) one-hot encode categorical predictors, replacing each one with several binary indicators, one for every level or category. Regression trees built with these indicators partition the discrete set of categorical levels by repeatedly removing one level at a time. Unfortunately, the vast majority of partitions cannot be built with this strategy, severely limiting BART's ability to partially pool data across groups of levels. Motivated by analyses of baseball data and neighborhood-level crime dynamics, we overcame this limitation by re-implementing BART with regression trees that can assign multiple levels to both branches of a decision tree node. To model spatial data aggregated into small regions, we further proposed a new decision rule prior that creates spatially contiguous regions by deleting a random edge from a random spanning tree of a suitably defined network. Our re-implementation, which is available in the flexBART package, often yields improved out-of-sample predictive performance and scales better to larger datasets than existing implementations of BART., Comment: Software available at https://github.com/skdeshpande91/flexBART

Supplemental material, sj-pdf-1-wmr-10.1177_0734242X20985608 for Influencing household-level waste-sorting and composting behaviour: What works? A systematic review (1995–2020) of waste management interventions by Aarti Sewak, Jeawon Kim, Sharyn Rundle-Thiele and Sameer Deshpande in Waste Management & Research

Supplemental material, sj-pdf-1-wmr-10.1177_0734242X20985608 for Influencing household-level waste-sorting and composting behaviour: What works? A systematic review (1995–2020) of waste management interventions by Aarti Sewak, Jeawon Kim, Sharyn Rundle-Thiele and Sameer Deshpande in Waste Management & Research

Many studies have reported associations between later-life cognition and socioeconomic position in childhood, young adulthood, and mid-life. However, the vast majority of these studies are unable to quantify how these associations vary over time and with respect to several demographic factors. Varying coefficient (VC) models, which treat the covariate effects in a linear model as nonparametric functions of additional effect modifiers, offer an appealing way to overcome these limitations. Unfortunately, state-of-the-art VC modeling methods require computationally prohibitive parameter tuning or make restrictive assumptions about the functional form of the covariate effects. In response, we propose VCBART, which estimates the covariate effects in a VC model using Bayesian Additive Regression Trees. With simple default hyperparameter settings, VCBART outperforms existing methods in terms of covariate effect estimation and prediction. Using VCBART, we predict the cognitive trajectories of 4,167 subjects from the Health and Retirement Study using multiple measures of socioeconomic position and physical health. We find that socioeconomic position in childhood and young adulthood have small effects that do not vary with age. In contrast, the effects of measures of mid-life physical health tend to vary with respect to age, race, and marital status. An R package implementing VCBART is available at https://github.com/skdeshpande91/VCBART, Comment: Updating acknowledgements (February 2022)

More than 1 million students play high school American football annually, but many health professionals have recently questioned its safety or called for its ban. These concerns have been partially driven by reports of chronic traumatic encephalopathy (CTE), increased risks of neurodegenerative disease, and associations between concussion history and later-life cognitive impairment and depression among retired professional football players. A recent observational study of a cohort of men who graduated from a Wisconsin high school in 1957 found no statistically significant harmful effects of playing high school football on a range of cognitive, psychological, and socio-economic outcomes measured at ages 35, 54, 65, and 72. Unfortunately, these findings may not generalize to younger populations, thanks to changes and improvements in football helmet technology and training techniques. In particular, these changes may have led to increased perceptions of safety but ultimately more dangerous styles of play, characterized by the frequent sub-concussive impacts thought to be associated with later-life neurological decline. In this work, we replicate the methodology of that earlier matched observational study using data from the National Longitudinal Study of Adolescent to Adult Health (Add Health). These include adolescent and family co-morbidities, academic experience, self-reported levels of general health and physical activity, and the score on the Add Health Picture Vocabulary Test. Our primary outcome is the CES-D score measured in 2008 when subjects were aged 24 -- 34 and settling into early adulthood. We also examine several secondary outcomes related to physical and psychological health, including suicidality. Our results can provide insight into the natural history of potential football-related decline and dysfunction., Comment: Updated tables summarizing the matches constructed

A potential causal relationship between head injuries sustained by NFL players and later-life neurological decline may have broad implications for participants in youth and high school football programs. However, brain trauma risk at the professional level may be different than that at the youth and high school levels and the long-term effects of participation at these levels is as-yet unclear. To investigate the effect of playing high school football on later life depression and cognitive functioning, we propose a retrospective observational study using data from the Wisconsin Longitudinal Study (WLS) of graduates from Wisconsin high schools in 1957. We compare 1,153 high school males who played varsity football to 2,751 male students who did not. 1,951 of the control subjects did not play any sport and the remaining 800 controls played a non-contact sport. We focus on two primary outcomes measured at age 65: a composite cognitive outcome measuring verbal fluency and memory and the modified CES-D depression score. To control for potential confounders we adjust for pre-exposure covariates such as IQ with matching and model-based covariate adjustment. We will conduct an ordered testing procedure that uses all 2,751 controls while controlling for possible unmeasured differences between students who played sports and those who did not. We will quantitatively assess the sensitivity of the results to potential unmeasured confounding. The study will also consider several secondary outcomes of clinical interest such as aggression and heavy drinking. The rich set of pre-exposure variables, relatively unbiased sampling, and longitudinal nature of the WLS dataset make the proposed analysis unique among related studies that rely primarily on convenience samples of football players with reported neurological symptoms., Comment: Prior to performing the proposed analysis, we will register this pre-analysis plan on clincialtrials.gov

Sharing water is a contentious environmental issue. Irrigation controls the majority of water resources, but international experiences illustrate that irrigators are reluctant to share their water. How can resource managers achieve acceptance of water-sharing policies to meet the changing needs of society? This study focuses on strategies for selling a political agenda related to water transfers from agriculture. A survey was conducted with 275 irrigators in the western Canadian province of Alberta to assess how attributes that define a water transfer influence irrigators’ acceptance rates. Results indicated that the purpose of the transfer was the most important attribute. Six segments emerged based on perceptions of attributes. Greenies, Water Savers, and Efficiency Savers considered environmental attributes, whereas Personal Gainers and Municipal Friends considered personal and municipal attributes. Nay Sayers rejected transfers regardless of attributes. These results provide recommendations for resource managers attempting to persuade irrigators to share their water. Refereed/Peer-reviewed