2,714 research outputs found
Nonparametric causal effects based on incremental propensity score interventions
Most work in causal inference considers deterministic interventions that set
each unit's treatment to some fixed value. However, under positivity violations
these interventions can lead to non-identification, inefficiency, and effects
with little practical relevance. Further, corresponding effects in longitudinal
studies are highly sensitive to the curse of dimensionality, resulting in
widespread use of unrealistic parametric models. We propose a novel solution to
these problems: incremental interventions that shift propensity score values
rather than set treatments to fixed values. Incremental interventions have
several crucial advantages. First, they avoid positivity assumptions entirely.
Second, they require no parametric assumptions and yet still admit a simple
characterization of longitudinal effects, independent of the number of
timepoints. For example, they allow longitudinal effects to be visualized with
a single curve instead of lists of coefficients. After characterizing these
incremental interventions and giving identifying conditions for corresponding
effects, we also develop general efficiency theory, propose efficient
nonparametric estimators that can attain fast convergence rates even when
incorporating flexible machine learning, and propose a bootstrap-based
confidence band and simultaneous test of no treatment effect. Finally we
explore finite-sample performance via simulation, and apply the methods to
study time-varying sociological effects of incarceration on entry into
marriage
Survivor-complier effects in the presence of selection on treatment, with application to a study of prompt ICU admission
Pre-treatment selection or censoring (`selection on treatment') can occur
when two treatment levels are compared ignoring the third option of neither
treatment, in `censoring by death' settings where treatment is only defined for
those who survive long enough to receive it, or in general in studies where the
treatment is only defined for a subset of the population. Unfortunately, the
standard instrumental variable (IV) estimand is not defined in the presence of
such selection, so we consider estimating a new survivor-complier causal
effect. Although this effect is generally not identified under standard IV
assumptions, it is possible to construct sharp bounds. We derive these bounds
and give a corresponding data-driven sensitivity analysis, along with
nonparametric yet efficient estimation methods. Importantly, our approach
allows for high-dimensional confounding adjustment, and valid inference even
after employing machine learning. Incorporating covariates can tighten bounds
dramatically, especially when they are strong predictors of the selection
process. We apply the methods in a UK cohort study of critical care patients to
examine the mortality effects of prompt admission to the intensive care unit,
using ICU bed availability as an instrument
Optimal doubly robust estimation of heterogeneous causal effects
Heterogeneous effect estimation plays a crucial role in causal inference,
with applications across medicine and social science. Many methods for
estimating conditional average treatment effects (CATEs) have been proposed in
recent years, but there are important theoretical gaps in understanding if and
when such methods are optimal. This is especially true when the CATE has
nontrivial structure (e.g., smoothness or sparsity). Our work contributes in
several main ways. First, we study a two-stage doubly robust CATE estimator and
give a generic model-free error bound, which, despite its generality, yields
sharper results than those in the current literature. We apply the bound to
derive error rates in nonparametric models with smoothness or sparsity, and
give sufficient conditions for oracle efficiency. Underlying our error bound is
a general oracle inequality for regression with estimated or imputed outcomes,
which is of independent interest; this is the second main contribution. The
third contribution is aimed at understanding the fundamental statistical limits
of CATE estimation. To that end, we propose and study a local polynomial
adaptation of double-residual regression. We show that this estimator can be
oracle efficient under even weaker conditions, if used with a specialized form
of sample splitting and careful choices of tuning parameters. These are the
weakest conditions currently found in the literature, and we conjecture that
they are minimal in a minimax sense. We go on to give error bounds in the
non-trivial regime where oracle rates cannot be achieved. Some finite-sample
properties are explored with simulations
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