92,919 research outputs found
Observational-Interventional Priors for Dose-Response Learning
Controlled interventions provide the most direct source of information for
learning causal effects. In particular, a dose-response curve can be learned by
varying the treatment level and observing the corresponding outcomes. However,
interventions can be expensive and time-consuming. Observational data, where
the treatment is not controlled by a known mechanism, is sometimes available.
Under some strong assumptions, observational data allows for the estimation of
dose-response curves. Estimating such curves nonparametrically is hard: sample
sizes for controlled interventions may be small, while in the observational
case a large number of measured confounders may need to be marginalized. In
this paper, we introduce a hierarchical Gaussian process prior that constructs
a distribution over the dose-response curve by learning from observational
data, and reshapes the distribution with a nonparametric affine transform
learned from controlled interventions. This function composition from different
sources is shown to speed-up learning, which we demonstrate with a thorough
sensitivity analysis and an application to modeling the effect of therapy on
cognitive skills of premature infants
Regression adjustments for estimating the global treatment effect in experiments with interference
Standard estimators of the global average treatment effect can be biased in
the presence of interference. This paper proposes regression adjustment
estimators for removing bias due to interference in Bernoulli randomized
experiments. We use a fitted model to predict the counterfactual outcomes of
global control and global treatment. Our work differs from standard regression
adjustments in that the adjustment variables are constructed from functions of
the treatment assignment vector, and that we allow the researcher to use a
collection of any functions correlated with the response, turning the problem
of detecting interference into a feature engineering problem. We characterize
the distribution of the proposed estimator in a linear model setting and
connect the results to the standard theory of regression adjustments under
SUTVA. We then propose an estimator that allows for flexible machine learning
estimators to be used for fitting a nonlinear interference functional form. We
propose conducting statistical inference via bootstrap and resampling methods,
which allow us to sidestep the complicated dependences implied by interference
and instead rely on empirical covariance structures. Such variance estimation
relies on an exogeneity assumption akin to the standard unconfoundedness
assumption invoked in observational studies. In simulation experiments, our
methods are better at debiasing estimates than existing inverse propensity
weighted estimators based on neighborhood exposure modeling. We use our method
to reanalyze an experiment concerning weather insurance adoption conducted on a
collection of villages in rural China.Comment: 38 pages, 7 figure
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