Replication data for: Multivariate Matching Methods That are Monotonic Imbalance Bounding (doi:10.7910/DVN/OMHQFP)

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Part 1: Document Description
Part 2: Study Description
Part 3: Data Files Description
Part 4: Variable Description
Part 5: Other Study-Related Materials
Entire Codebook

Document Description

Citation

Title:

Replication data for: Multivariate Matching Methods That are Monotonic Imbalance Bounding

Identification Number:

doi:10.7910/DVN/OMHQFP

Distributor:

Harvard Dataverse

Date of Distribution:

2011-08-23

Version:

6

Bibliographic Citation:

Iacus, Stefano M.; King, Gary; Porro, Giuseppe, 2011, "Replication data for: Multivariate Matching Methods That are Monotonic Imbalance Bounding", https://doi.org/10.7910/DVN/OMHQFP, Harvard Dataverse, V6, UNF:5:OnpNwQCslLvU0j0suCIJNg== [fileUNF]

Study Description

Citation

Title:

Replication data for: Multivariate Matching Methods That are Monotonic Imbalance Bounding

Identification Number:

doi:10.7910/DVN/OMHQFP

Authoring Entity:

Iacus, Stefano M. (Department of Economics, Business and Statistics, University of Milan)

King, Gary (Institute for Quantitative Social Science, Harvard University)

Porro, Giuseppe (Department of Economics and Statistics, University of Trieste)

Date of Production:

2011

Distributor:

Harvard Dataverse

Distributor:

Harvard Dataverse

Date of Deposit:

2011-08-23

Date of Distribution:

2011

Holdings Information:

https://doi.org/10.7910/DVN/OMHQFP

Study Scope

Keywords:

Social Sciences, Causal inference; EPBR; Matching

Abstract:

We introduce a new "Monotonic Imbalance Bounding" (MIB) class of matching methods for causal inference with a surprisingly large number of attractive statistical properties. MIB generalizes and extends in several new directions the only existing class, "Equal Percent Bias Reducing" (EPBR), which is designed to satisfy weaker properties and only in expectation. We also offer strategies to obtain specific members of the MIB class, and analyze in more detail a member of this class, called Coarsened Exact Matching, whose properties we analyze from this new perspective. We offer a variety of analytical results and numerical simulations that demonstrate how members of the MIB class can dramatically improve inferences relative to EPBR-based matching methods. <br /><br /> See also: <a href="http://gking.harvard.edu/category/research-interests/methods/causal-inference" target="_blank">Casual Inference</a>

Methodology and Processing

Sources Statement

Data Access

Notes:

This dataset is made available without information on how it can be used. You should communicate with the Contact(s) specified before use.

Other Study Description Materials

Related Publications

Citation

Title:

Iacus, Stefano M, Gary King, and Giuseppe Porro. 2011. Multivariate Matching Methods That are Monotonic Imbalance Bounding. Journal of the American Statistical Association 106, no. 493: 345-361: <a href="http://gking.harvard.edu/files/abs/cem-math-abs.shtml" target="_blank"> Link to article</a>

Bibliographic Citation:

Iacus, Stefano M, Gary King, and Giuseppe Porro. 2011. Multivariate Matching Methods That are Monotonic Imbalance Bounding. Journal of the American Statistical Association 106, no. 493: 345-361: <a href="http://gking.harvard.edu/files/abs/cem-math-abs.shtml" target="_blank"> Link to article</a>

File Description--f2304837

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  • No. of variables per record: 61

  • Type of File: text/tab-separated-values

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Variable Description

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Variables

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Total children

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proportion female children

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Percent white (total population)

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Percent high school grad rate (age 25p)

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Percent college grad rate (age 25p)

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Proportion in state who favor allowing abortion

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Proportion in state who favor more spending on services

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Variable Format: numeric

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Proportion in state who favor more spending on defense

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Proportion in state who favor more crime spending

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Proportion in state who favor laws protecting homosexuals

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Percent Christian (in state) 2001 CUNY

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aauw

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