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The mysteries multiply as we look through the literature but some answers are still forthcoming.

The mystery continues, leading us down an algorithmic path filled with broken sticks.

A parameter collapses, a sophisticated model looms afoot and we find the start of a trail of clues in this unexpected mystery.

An overview of two of the main obstacles to causal inference: Confounding and Selection Bias

An overview of the conditions required for instrumental variables and an example application in the built environment

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Causal Inference is the foundation of most scientific inquiry. In this simple case study we walk through several key concepts involved …

The presence of specific urban environment resources such as food vendors and recreation centers may impact diet and physical activity, …

Recent Publications

Built environment features (BEFs) refer to aspects of the human constructed environment, which may in turn support or restrict health …

The rstap package implements Bayesian spatial temporal aggregated predictor models in R using the probabilistic programming language …