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Causal inference at the intersection of many state of the art methods Goetghebeur, Els
Description
Methods and techniques available for causal inference have exploded over the past decade. Penetrating into this literature is particularly hard for the practicing statistician, since the material is challenging both at the conceptual and technical level. At the heart of causal inference lies an extra dimension of abstraction in the form of latent variables, also called potential outcomes, representing the possibly counterfactual answers to the question `what if exposure had been set to (different) level x’. The endeavor is worthwhile however since causal claims are often the target and they are frequently made in the medical literature, sometimes based on overly simplistic analyses.
The evidence in this setting is not brought in through direct observation of subject-specific potential outcome measures, but is typically derived from assumed links (or independence) between those latent variables and the different observed exposure-response data conditional on covariates. Once assumptions and the target of estimation are clear, most causal effect estimation methods have a specific core, but equally rely on methods and issues which form the topic of other working groups in STRATOS. Specifically:
TG8 outcomes may be of different types; binary, continuous, right censored survival (competing risks), longitudinal…
TG2 outcome regression and propensity score regression involved in causal inference must consider the impact of model selection (and prediction error) etc. while accounting for the special nature of covariates that are confounders
TG1 missing data often occur, and in some sense the `alternative’ potential outcomes can be seen as missing data themselves
TG3 adapted descriptive statistics are advised
TG5 studies are ideally designed with causal inference in mind
In this talk we will present the essence of the causal inference approach of TG7 and point to important `potential’ links with the work of other topics groups in STRATOS.
Item Metadata
| Title |
Causal inference at the intersection of many state of the art methods
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| Creator | |
| Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
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| Date Issued |
2016-07-04T17:39
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| Description |
Methods and techniques available for causal inference have exploded over the past decade. Penetrating into this literature is particularly hard for the practicing statistician, since the material is challenging both at the conceptual and technical level. At the heart of causal inference lies an extra dimension of abstraction in the form of latent variables, also called potential outcomes, representing the possibly counterfactual answers to the question `what if exposure had been set to (different) level x’. The endeavor is worthwhile however since causal claims are often the target and they are frequently made in the medical literature, sometimes based on overly simplistic analyses.
The evidence in this setting is not brought in through direct observation of subject-specific potential outcome measures, but is typically derived from assumed links (or independence) between those latent variables and the different observed exposure-response data conditional on covariates. Once assumptions and the target of estimation are clear, most causal effect estimation methods have a specific core, but equally rely on methods and issues which form the topic of other working groups in STRATOS. Specifically: TG8 outcomes may be of different types; binary, continuous, right censored survival (competing risks), longitudinal… TG2 outcome regression and propensity score regression involved in causal inference must consider the impact of model selection (and prediction error) etc. while accounting for the special nature of covariates that are confounders TG1 missing data often occur, and in some sense the `alternative’ potential outcomes can be seen as missing data themselves TG3 adapted descriptive statistics are advised TG5 studies are ideally designed with causal inference in mind In this talk we will present the essence of the causal inference approach of TG7 and point to important `potential’ links with the work of other topics groups in STRATOS. |
| Extent |
47 minutes
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| Subject | |
| Type | |
| File Format |
video/mp4
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| Language |
eng
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| Notes |
Author affiliation: University of Ghent
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| Series | |
| Date Available |
2017-02-04
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| Provider |
Vancouver : University of British Columbia Library
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| Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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| DOI |
10.14288/1.0340469
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| URI | |
| Affiliation | |
| Peer Review Status |
Unreviewed
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| Scholarly Level |
Faculty
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| Rights URI | |
| Aggregated Source Repository |
DSpace
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International