Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A nonparametric super-efficient estimator of the average treatment
  effect   Doubly robust estimators of causal effects are a popular means of estimating
causal effects. Such estimators combine an estimate of the conditional mean of
the outcome given treatment and confounders (the so-called outcome regression)
with an estimate of the conditional probability of treatment given confounders
(the propensity score) to generate an estimate of the effect of interest. In
addition to enjoying the double-robustness property, these estimators have
additional benefits. First, flexible regression tools, such as those developed
in the field of machine learning, can be utilized to estimate the relevant
regressions, while the estimators of the treatment effects retain desirable
statistical properties. Furthermore, these estimators are often statistically
efficient, achieving the lower bound on the variance of regular, asymptotically
linear estimators. However, in spite of their asymptotic optimality, in
problems where causal estimands are weakly identifiable, these estimators may
behave erratically. We propose two new estimation techniques for use in these
challenging settings. Our estimators build on two existing frameworks for
efficient estimation: targeted minimum loss estimation and one-step estimation.
However, rather than using an estimate of the propensity score in their
construction, we instead opt for an alternative regression quantity when
building our estimators: the conditional probability of treatment given the
conditional mean outcome. We discuss the theoretical implications and
demonstrate the estimators' performance in simulated and real data.

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The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
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The categories of this paper are: quant-ph
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The categories of this paper are: adap-org
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The categories of this paper are: cs
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The categories of this paper are: q-bio
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: cmp-lg