Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Sävje, Fredrik"'
We describe a new design-based framework for drawing causal inference in randomized experiments. Causal effects in the framework are defined as linear functionals evaluated at potential outcome functions. Knowledge and assumptions about the potential
Externí odkaz:
http://arxiv.org/abs/2210.08698
Unbiased and consistent variance estimators generally do not exist for design-based treatment effect estimators because experimenters never observe more than one potential outcome for any unit. The problem is exacerbated by interference and complex e
Externí odkaz:
http://arxiv.org/abs/2112.01709
The regression discontinuity (RD) design offers identification of causal effects under weak assumptions, earning it a position as a standard method in modern political science research. But identification does not necessarily imply that causal effect
Externí odkaz:
http://arxiv.org/abs/2109.14526
We argue that randomized controlled trials (RCTs) are special even among settings where average treatment effects are identified by a nonparametric unconfoundedness assumption. This claim follows from two results of Robins and Ritov (1997): (1) with
Externí odkaz:
http://arxiv.org/abs/2108.11342
Autor:
Sävje, Fredrik
A common assumption in causal inference is that random treatment assignment ensures that potential outcomes are independent of treatment, or in one word, unconfoundedness. This paper highlights that randomization and unconfoundedness are separate pro
Externí odkaz:
http://arxiv.org/abs/2107.14197
Autor:
Sävje, Fredrik
Exposure mappings facilitate investigations of complex causal effects when units interact in experiments. Current methods require experimenters to use the same exposure mappings both to define the effect of interest and to impose assumptions on the i
Externí odkaz:
http://arxiv.org/abs/2103.06471
A bipartite experiment consists of one set of units being assigned treatments and another set of units for which we measure outcomes. The two sets of units are connected by a bipartite graph, governing how the treated units can affect the outcome uni
Externí odkaz:
http://arxiv.org/abs/2103.06392
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
The design of experiments involves a compromise between covariate balance and robustness. This paper provides a formalization of this trade-off and describes an experimental design that allows experimenters to navigate it. The design is specified by
Externí odkaz:
http://arxiv.org/abs/1911.03071
Autor:
Sävje, Fredrik
The paper shows that matching without replacement on propensity scores produces estimators that generally are inconsistent for the average treatment effect of the treated. To achieve consistency, practitioners must either assume that no units exist w
Externí odkaz:
http://arxiv.org/abs/1907.07288