Please use this identifier to cite or link to this item:
Title: Causal inference for multiple treatments via sufficiency and ratios of generalized propensity scores
Authors: Yatracos, Yannis G. 
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.
Rights: 2012 Yatracos Yannis G.
Appears in Collections:Εκθέσεις/Reports

Files in This Item:
File Description SizeFormat 
report.pdf154.04 kBAdobe PDFView/Open
Show full item record

Page view(s) 10

Last Week
Last month
checked on Jul 23, 2017

Download(s) 20

checked on Jul 23, 2017

Google ScholarTM


This item is licensed under a Creative Commons License Creative Commons