Zobrazeno 1 - 10
of 148
pro vyhledávání: '"ARONOW, PETER"'
This paper presents methods for analyzing spatial experiments when complex spillovers, displacement effects, and other types of "interference" are present. We present a robust, design-based approach to analyzing effects in such settings. The design-b
Externí odkaz:
http://arxiv.org/abs/2106.15081
Autor:
Wang, J. Sophia, Aronow, Peter M.
We consider the properties of listwise deletion when both $n$ and the number of variables grow large. We show that when (i) all data has some idiosyncratic missingness and (ii) the number of variables grows superlogarithmically in $n$, then, for larg
Externí odkaz:
http://arxiv.org/abs/2101.11470
Autor:
Aronow, Peter M., Sävje, Fredrik
Publikováno v:
J. Amer. Statist. Assoc. (2020) 115: 482--485
Book review published as: Aronow, Peter M. and Fredrik S\"avje (2020), "The Book of Why: The New Science of Cause and Effect." Journal of the American Statistical Association, 115: 482-485.
Externí odkaz:
http://arxiv.org/abs/2003.11635
We present current methods for estimating treatment effects and spillover effects under "interference", a term which covers a broad class of situations in which a unit's outcome depends not only on treatments received by that unit, but also on treatm
Externí odkaz:
http://arxiv.org/abs/2001.05444
Autor:
Aronow, Peter M., Lee, Donald K. K.
Given samples $x_1,\cdots,x_n$, it is well known that any sample median value (not necessarily unique) minimizes the absolute loss $\sum_{i=1}^n |q-x_i|$. Interestingly, we show that the minimizer of the loss $\sum_{i=1}^n|q-x_i|^{1+\epsilon}$ exhibi
Externí odkaz:
http://arxiv.org/abs/1807.03462
We investigate large-sample properties of treatment effect estimators under unknown interference in randomized experiments. The inferential target is a generalization of the average treatment effect estimand that marginalizes over potential spillover
Externí odkaz:
http://arxiv.org/abs/1711.06399
Autor:
Aronow, Peter M.
King and Roberts (2015, KR) claim that a disagreement between robust and classical standard errors exposes model misspecification. We emphasize that KR's claim only generally applies to parametric models: models that assume a restrictive form of the
Externí odkaz:
http://arxiv.org/abs/1609.01774
We develop a general framework for conducting inference on the mean of dependent random variables given constraints on their dependency graph. We establish the consistency of an oracle variance estimator of the mean when the dependency graph is known
Externí odkaz:
http://arxiv.org/abs/1602.00359