Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/3397
Title: | Causal inference for multiple treatments via sufficiency and ratios of generalized propensity scores |
Authors: | Yatracos, Yannis G. |
metadata.dc.contributor.other: | Γιατράκος, Γιάννης |
Major Field of Science: | Social Sciences |
Field Category: | Economics and Business |
Keywords: | Causal Inference;Generalized Propensity Scores;Matching;Minimal Sufficient Statistic |
Issue Date: | 2012 |
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. |
URI: | https://hdl.handle.net/20.500.14279/3397 |
Rights: | 2012 Yatracos Yannis G. |
Type: | Report |
Affiliation : | Cyprus University of Technology |
Appears in Collections: | Εκθέσεις/Reports |
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report.pdf | 154.04 kB | Adobe PDF | View/Open |
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