- This event has passed.
Phillip HEILER (Aarhus University) – “Estimating Heterogeneous Bounds for Treatment Effects under Sample Selection and Non-response”
Time: 12:15 pm – 13:30 pm
Date: 14th of December 2021
Room : 3001
Abstract: In this paper we propose a method for nonparametric estimation and inference for heterogeneous bounds for causal effect parameters in general sample selection models where the initial treatment can affect whether a post-intervention outcome is observed or not. The original treatment selection can be confounded by observable covariates while the outcome selection can be affected by both observables and unobservables. The method provides conditional effect bounds as functions depending on policy relevant pre-treatment variables. It allows for conducting valid statistical inference on the unidentified conditional effect curves. We use a semiparametric de-biased machine learning approach that can accommodate flexible functional forms and high-dimensional observed confounding variables in both treatment and outcome selection process.
Organizers:
Benoît SCHMUTZ (Pôle d’économie du CREST)
Anthony STRITTMATTER (Pôle d’économie du CREST)
Sponsors:
CREST
Time: 12:15 pm – 13:30 pm
Date: 14th of December 2021
Room : 3001
Phillip HEILER (Aarhus University) – “Estimating Heterogeneous Bounds for Treatment Effects under Sample Selection and Non-response”
Abstract: In this paper we propose a method for nonparametric estimation and inference for heterogeneous bounds for causal effect parameters in general sample selection models where the initial treatment can affect whether a post-intervention outcome is observed or not. The original treatment selection can be confounded by observable covariates while the outcome selection can be affected by both observables and unobservables. The method provides conditional effect bounds as functions depending on policy relevant pre-treatment variables. It allows for conducting valid statistical inference on the unidentified conditional effect curves. We use a semiparametric de-biased machine learning approach that can accommodate flexible functional forms and high-dimensional observed confounding variables in both treatment and outcome selection process.
Organizers:
Benoît SCHMUTZ (Pôle d’économie du CREST)
Anthony STRITTMATTER (Pôle d’économie du CREST)
Sponsors:
CREST