Identifying Treatment Effects in the Presence of Confounded Types

Kédagni, Désiré

doi:10.2139/ssrn.3064230

Cited by 5 publications

(5 citation statements)

References 54 publications

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“…For a recent extension, seeHoagland (2019). Other related papers, albeit not necessarily focused on LATE, areBattistin et al (2014),Chalak (2017), DiTraglia and García-Jimeno (2018),Jiang and Ding (2019),Kasahara and Shimotsu (2019),Kedagni (2019) andYanagi (2019).…”

mentioning

confidence: 99%

Bounding Program Benefits When Participation is Misreported

Tommasi

Zhang

2020

SSRN Journal

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mentioning

confidence: 99%

Bounding Program Benefits When Participation is Misreported

Tommasi

Zhang

2020

SSRN Journal

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“…Lee (2009) rules out a zero effect after accounting for the loss in labor market experience generated by the extra education acquired by Job Corps participants. Chen & Flores (2015) find that the LAT E OO on hourly wages four years after randomization is between 5.7% and 5 Other important contributions are made by Huber (2014), Steinmayr (2014), Blanco et al (2017) and Kedagni (2018). 6 For example, significant contributions are made by Schochet et al (2001), Schochet et al (2008), Flores-Lagunes et al (2010), Flores et al (2012), Blanco et al (2013a), Blanco et al (2013b), Blanco et al (2017) and Chen et al (2017). 13.9% for the entire population and between 7.7% and 17.5% for the non-Hispanic population under monotonicity and mean dominance assumptions.…”

Section: Introductionmentioning

confidence: 99%

Sharp Bounds on the MTE With Sample Selection

Possebom

2019

SSRN Journal

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I analyze treatment effects in situations when agents endogenously select into the treatment group and into the observed sample. As a theoretical contribution, I propose pointwise sharp bounds for the marginal treatment effect (MTE) of interest within the always-observed subpopulation under monotonicity assumptions. Moreover, I impose an extra mean dominance assumption to tighten the previous bounds. I further discuss how to identify those bounds when the support of the propensity score is either continuous or discrete. Using these results, I estimate bounds for the MTE of the Job Corps Training Program on hourly wages for the always-employed subpopulation and find that it is decreasing in the likelihood of attending the program within the Non-Hispanic group. For example, the Average Treatment Effect on the Treated is between $.33 and $.99 while the Average Treatment Effect on the Untreated is between $.71 and $3.00.

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“…However, all the above methods still assumed no unmeasured confounding between the treatment and the survival process, which is true in randomized experiments but does not hold in observational studies. Few literature focused on identification of causal effects with principal stratification in observational studies (Kédagni, 2021).…”

Section: Introductionmentioning

confidence: 99%

Identification and Estimation of Heterogeneous Survivor Average Causal Effect in Observational Studies

Deng¹,

Guo²,

Chang³

et al. 2021

Preprint

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Clinical studies are often encountered with truncation-by-death issues, which render the outcomes undefined. Statistical analysis based only on observed survivors may lead to biased results because the characters of survivors may differ greatly between treatment groups. Under the principal stratification framework, a meaningful causal parameter, the survivor average causal effect, in the always-survivor group can be defined. This causal parameter may not be identifiable in observational studies where the treatment assignment and the survival or outcome process are confounded by unmeasured features. In this paper, we propose a new method to deal with unmeasured confounding when the outcome is truncated by death. First, a new method is proposed to identify the heterogeneous conditional survival average causal effect based on a substitutional variable under monotonicity. Second, under additional assumptions, the survivor average causal effect on the whole population is identified. Furthermore, we consider estimation and inference for the conditional survivor average causal effect based on parametric and nonparametric methods. The proposed method can be used for post marketing drug safety or efficiency by utilizing real world data.

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Identifying Treatment Effects in the Presence of Confounded Types

Cited by 5 publications

References 54 publications

Bounding Program Benefits When Participation is Misreported

Bounding Program Benefits When Participation is Misreported

Sharp Bounds on the MTE With Sample Selection

Identification and Estimation of Heterogeneous Survivor Average Causal Effect in Observational Studies

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