A new approach to sensitivity analysis in linear SEMs using stochastic optimization and the $R^2$-calculus

Sensitivity analysis is widely recognized as a critical step in an observational study but is seldom found in applications. One reason for its underuse is the various forms of model, inference, and interpretation in divergent literatures. This talk …

Vignette of an R package for instrumental variable regression.

Many partial identification problems can be characterized by the optimal value of a function over a set where both the function and set need to be estimated by empirical data. Despite some progress for convex problems, statistical inference in this …

Optimal individualized treatment rules try to assign the best treatment to every individual, but it may be very sensitive to unmeasured confounding bias for groups of people exhibiting small treatment effect in the observational study. We give a …

Rosenbaum’s sensitivity analysis framework has several limitations: 1. It is mostly applicable to matched observational studies; 2. It only tests the sharp null hypothesis; 3. It assumes treatment effect homogeneity to obtain a confidence interval of …

This paper proposes a new method called 'cross-screening' to increase the power of sensitivity analysis when multiple causal hypotheses need to be tested simultaneously.

2024

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