Zobrazeno 1 - 10
of 173
pro vyhledávání: '"Hansen, Ben B."'
Autor:
Wang, Xinhe, Hansen, Ben B.
Randomized controlled trials (RCTs) are used to evaluate treatment effects. When individuals are grouped together, clustered RCTs are conducted. Stratification is recommended to reduce imbalance of baseline covariates between treatment and control. I
Externí odkaz:
http://arxiv.org/abs/2406.10473
Autor:
Hansen, Ben B.
This paper characterizes the precision of index estimation as it carries over into precision of matching. In a model assuming Gaussian covariates and making best-case assumptions about matching quality, it sharply characterizes average and worst-case
Externí odkaz:
http://arxiv.org/abs/2301.04109
Autor:
Lycurgus, Timothy, Hansen, Ben B.
We present an aggregation scheme that increases power in randomized controlled trials and quasi-experiments when the intervention possesses a robust and well-articulated theory of change. Longitudinal data analyzing interventions often include multip
Externí odkaz:
http://arxiv.org/abs/2107.13070
Publikováno v:
Journal of Educational and Behavioral Statistics, 43(1), 3-31
In causal matching designs, some control subjects are often left unmatched, and some covariates are often left unmodeled. This article introduces "rebar," a method using high-dimensional modeling to incorporate these commonly discarded data without s
Externí odkaz:
http://arxiv.org/abs/1505.04697
Autor:
Sales, Adam C., Hansen, Ben B.
Publikováno v:
Journal of Educational and Behavioral Statistics. 2020;45(2):143-174
Conventionally, regression discontinuity analysis contrasts a univariate regression's limits as its independent variable, $R$, approaches a cut-point, $c$, from either side. Alternative methods target the average treatment effect in a small region ar
Externí odkaz:
http://arxiv.org/abs/1403.5478
The sensitivity of linear regression coefficients' confidence limits to the omission of a confounder
Publikováno v:
Annals of Applied Statistics 2010, Vol. 4, No. 2, 849-870
Omitted variable bias can affect treatment effect estimates obtained from observational data due to the lack of random assignment to treatment groups. Sensitivity analyses adjust these estimates to quantify the impact of potential omitted variables.
Externí odkaz:
http://arxiv.org/abs/0905.3463
Akademický článek
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Autor:
Hansen, Ben B., Bowers, Jake
Publikováno v:
Statistical Science 2008, Vol. 23, No. 2, 219-236
In randomized experiments, treatment and control groups should be roughly the same--balanced--in their distributions of pretreatment variables. But how nearly so? Can descriptive comparisons meaningfully be paired with significance tests? If so, shou
Externí odkaz:
http://arxiv.org/abs/0808.3857
Publikováno v:
Journal of Educational and Behavioral Statistics, 2018 Feb 01. 43(1), 3-31.
Externí odkaz:
https://www.jstor.org/stable/26447871
Publikováno v:
Bernoulli, 2005 Aug 01. 11(4), 571-590.
Externí odkaz:
https://www.jstor.org/stable/3318887