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Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/jspui/handle/10488/5127

Title: Causal inference for multiple treatments via sufficiency and ratios of generalized propensity scores
Authors: Yatracos, Yannis G.
???metadata.dc.subject.field???: Social Sciences
???metadata.dc.subject.category???: Economics and Business
Keywords: Causal Inference
Generalized Propensity Scores
Minimal Sufficient Statistic
Issue Date: 2012
Publisher: Cyprus University of Technology, Faculty of Management and Economics
Abstract: The coarsest balancing score for multiple treatments T is the minimal sufficient statistic s for the covariates' distributions, DT ; of populations {Pt; t ∈ T } receiving each treatment t ∈ T : A unit in Pr with covariates x is a good match with respect to T for a unit in Pt with covariates y; when x and y provide similar information for DT ; i.e. when s(x) ≈ s(y): For finite, countably finite and often continuous treatments, s(x) is shown to be equivalent to ~e; a vector of propensities' ratios. The units in {Pt; t ∈ T } can be divided into subpopulations where causal comparisons are simultaneously valid. Satisfactory s-matchings are obtained for simulated covariates in R3: The use of ~e's estimate rather than s's estimate allows to avoid the x-curse of dimensionality, but the available data's size in each case is critical for the final choice.
???metadata.dc.type.*???: Technical Report
???metadata.dc.rights???: 2012 Yatracos Yannis G.
???metadata.dc.subject.collaboration???: Cyprus University of Technology
???metadata.dc.subject.country???: Cyprus
???metadata.dc.subject.journals???: Open Access
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