Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Causal Inference in High Dimensions -- Without Sparsity   We revisit the classical causal inference problem of estimating the average
treatment effect in the presence of fully observed, high dimensional
confounding variables, where the number of confounders $d$ is of the same order
as the sample size $n$. To make the problem tractable, we posit a generalized
linear model for the effect of the confounders on the treatment assignment and
outcomes, but do not assume any sparsity. Instead, we only require the
magnitude of confounding to remain non-degenerate. Despite making parametric
assumptions, this setting is a useful surrogate for some machine learning
methods used to adjust for confounding in two-stage methods. In particular, the
estimation of the first stage adds variance that does not vanish, forcing us to
confront terms in the asymptotic expansion that normally are brushed aside as
finite sample defects. Using new theoretical results and simulations, we
compare G-computation, inverse propensity weighting (IPW), and two common
doubly robust estimators -- augmented IPW (AIPW) and targeted maximum
likelihood estimation (TMLE). When the outcome model estimates are unbiased,
G-computation outperforms the other estimators in both bias and variance. Among
the doubly robust estimators, the TMLE estimator has the lowest variance.
Existing theoretical results do not explain this advantage because the TMLE and
AIPW estimators have the same asymptotic influence function. However, our model
emphasizes differences in performance between these estimators beyond
first-order asymptotics.

18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
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The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
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The categories of this paper are: bayes-an