Zobrazeno 1 - 10
of 364
pro vyhledávání: '"Imbens, Guido W."'
In his seminal work in 1923, Neyman studied the variance estimation problem for the difference-in-means estimator of the average treatment effect in completely randomized experiments. He proposed a variance estimator that is conservative in general a
Externí odkaz:
http://arxiv.org/abs/2409.12498
Autor:
Imbens, Guido W., Viviano, Davide
This paper studies inference on treatment effects in panel data settings with unobserved confounding. We model outcome variables through a factor model with random factors and loadings. Such factors and loadings may act as unobserved confounders: whe
Externí odkaz:
http://arxiv.org/abs/2312.00955
Autor:
Bajari, Patrick, Burdick, Brian, Imbens, Guido W., Masoero, Lorenzo, McQueen, James, Richardson, Thomas, Rosen, Ido M.
In this study we introduce a new class of experimental designs. In a classical randomized controlled trial (RCT), or A/B test, a randomly selected subset of a population of units (e.g., individuals, plots of land, or experiences) is assigned to a tre
Externí odkaz:
http://arxiv.org/abs/2112.13495
We develop new semiparametric methods for estimating treatment effects. We focus on settings where the outcome distributions may be thick tailed, where treatment effects may be small, where sample sizes are large and where assignment is completely ra
Externí odkaz:
http://arxiv.org/abs/2109.02603
We propose a new estimator for average causal effects of a binary treatment with panel data in settings with general treatment patterns. Our approach augments the popular two-way-fixed-effects specification with unit-specific weights that arise from
Externí odkaz:
http://arxiv.org/abs/2107.13737
Publikováno v:
In Journal of Econometrics March 2024 240(2)
Autor:
Arkhangelsky, Dmitry, Imbens, Guido W.
We study identification and estimation of causal effects in settings with panel data. Traditionally researchers follow model-based identification strategies relying on assumptions governing the relation between the potential outcomes and the observed
Externí odkaz:
http://arxiv.org/abs/1909.09412
Autor:
Imbens, Guido W.
In this essay I discuss potential outcome and graphical approaches to causality, and their relevance for empirical work in economics. I review some of the work on directed acyclic graphs, including the recent "The Book of Why," by Pearl and MacKenzie
Externí odkaz:
http://arxiv.org/abs/1907.07271
We present a new estimator for causal effects with panel data that builds on insights behind the widely used difference in differences and synthetic control methods. Relative to these methods we find, both theoretically and empirically, that this "sy
Externí odkaz:
http://arxiv.org/abs/1812.09970
Publikováno v:
The American Economic Review, 2021 Dec 01. 111(12), 4088-4118.
Externí odkaz:
https://www.jstor.org/stable/27086719