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Activity Number:
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658
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
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Sponsor:
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Biopharmaceutical Section
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Abstract #318998
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Title:
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Finding Common Support for Causal Inference Through Largest Connected Components
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Author(s):
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Sharif Mahmood* and Michael Higgins
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Companies:
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Kansas State University and Kansas State University
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Keywords:
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Causal inference ;
Common support ;
Largest connected component
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Abstract:
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Finding treatment effects in observational studies is complicated by the need to control for confounders. This is often performed by using prognostically important covariates to form groups of similar units containing both treatment and control units (e.g. statistical matching) and/or by modeling responses through interpolation. Hence, treatment effects are only reliably estimated for a subpopulation under which a common support assumption holds---one in which treatment and control covariate spaces overlap. Given a distance metric measuring dissimilarity between units, we use techniques in graph theory to find common support. We construct an adjacency graph where edges are drawn between similar treated and control units. We then determine regions of common support by finding the largest connected components (LCC) of this graph. We show that LCC improves on existing methods by efficiently constructing regions that preserve clustering in the data while ensuring interpretability of the region through the distance metric.
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Authors who are presenting talks have a * after their name.
