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Part 1: Document Description
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Citation |
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Title: |
Replication data for: Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference |
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Identification Number: |
doi:10.7910/DVN/RWUY8G |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2007-11-28 |
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Version: |
5 |
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Bibliographic Citation: |
Ho, Daniel E.; Imai, Kosuke; King, Gary; Stuart, Elizabeth A., 2007, "Replication data for: Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference", https://doi.org/10.7910/DVN/RWUY8G, Harvard Dataverse, V5, UNF:3:QV0mYCd8eV+mJgWDnYct5g== [fileUNF] |
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Citation |
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Title: |
Replication data for: Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference |
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Identification Number: |
doi:10.7910/DVN/RWUY8G |
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Authoring Entity: |
Ho, Daniel E. (Stanford Law School) |
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Imai, Kosuke (Princeton University) |
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King, Gary (Harvard University) |
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Stuart, Elizabeth A. (Johns Hopkins Bloomberg School of Public Health) |
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Date of Production: |
2006 |
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Distributor: |
Harvard Dataverse |
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Distributor: |
Harvard Dataverse |
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Date of Deposit: |
2006 |
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Date of Distribution: |
2007 |
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Holdings Information: |
https://doi.org/10.7910/DVN/RWUY8G |
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Study Scope |
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Keywords: |
Social Sciences |
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Abstract: |
Although published works rarely include causal estimates from more than a few model specifications, authors usually choose the presented estimates from numerous trial runs readers never see. Given the often large variation in estimates across choices of control variables, functional forms, and other modeling assumptions, how can researchers ensure that the few estimates presented are accurate or representative? How do readers know that publications are not merely demonstrations that it is possible to find a specification that fits the author’s favorite hypothesis? And how do we evaluate or even define statistical properties like unbiasedness or mean squared error when no unique model or estimator even exists? Matching methods, which offer the promise of causal inference with fewer assumptions, constitute one possible way forward, but crucial results in this fast-growing methodological literature are often grossly misinterpreted. We explain how to avoid these misinterpretations and propose a unified approach that makes it possible for researchers to preprocess data with matching (such as with the easy-to-use software we offer) and then to apply the best parametric techniques they would have used anyway. This procedure makes parametric models produce more accurate and considerably less model-dependent causal inferences. <br /> <br /> See also: <a href= "http://gking.harvard.edu/category/research-interests/methods/causal-inference" target="_blank">Causal Inference</a> |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
This dataset is made available without information on how it can be used. You should communicate with the Contact(s) specified before use. |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
Ho, Daniel, Kosuke Imai, Gary King, and Elizabeth Stuart. 2007. Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference. Political Analysis 15: 199–236: <a href= "http://gking.harvard.edu/files/abs/matchp-abs.shtml" target="_blank">Link to the article</a> and <a href= "http://nrs.harvard.edu/urn-3:HUL.InstRepos:4214880" target="_blank">Link to DASH</a> |
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Bibliographic Citation: |
Ho, Daniel, Kosuke Imai, Gary King, and Elizabeth Stuart. 2007. Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference. Political Analysis 15: 199–236: <a href= "http://gking.harvard.edu/files/abs/matchp-abs.shtml" target="_blank">Link to the article</a> and <a href= "http://nrs.harvard.edu/urn-3:HUL.InstRepos:4214880" target="_blank">Link to DASH</a> |
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File Description--f108265 |
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File: FDA-carpenter.tab |
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Notes: |
UNF:3:sACwzEY0GubtQd7DR15xjQ== |
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Carpenter's data read by matchfda.R; selected variables from full data set, for table 1 |
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File Description--f108268 |
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File: Figure1Data.tab |
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Notes: |
UNF:3:Cx0MS0pOjxPVmz14Za9VXg== |
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Data file for Figure 1 |
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File Description--f108272 |
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File: Visibility-Koch.tab |
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Notes: |
UNF:3:o5hYG/6Kh7jI2AvKOeN+Ug== |
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Koch's data; selected variables from full data set |
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List of Variables: | |
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Variables |
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f108265 Location: |
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f108272 Location: |
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Label: |
FDA-Carpenter.csv |
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Text: |
Carpenter's data read by matchfda.R; selected variables from full data set, for table 1 |
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Notes: |
text/plain; charset=US-ASCII |
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Label: |
fdadens.pdf |
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Text: |
Figure 2 (FDA) |
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Notes: |
application/pdf |
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Label: |
fdafigure.R |
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Text: |
R program to create figure 2 (FDA) |
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Notes: |
text/plain; charset=US-ASCII |
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Label: |
fdatable.R |
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Text: |
R program to create the table 1 (FDA) |
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Notes: |
text/plain; charset=US-ASCII |
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Label: |
Figure1.R |
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Text: |
R program to create Figure 1 |
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Notes: |
text/plain; charset=US-ASCII |
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Figure1.Rdata |
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Text: |
Data files for figure 1, R file format |
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Notes: |
application/x-rlang-transport |
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Figure1Data.txt |
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Text: |
Data files for figure 1, text file format |
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Notes: |
text/plain; charset=US-ASCII |
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Label: |
fn.R |
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Text: |
R program with functions used in matchfda.R and koch.R for table 1 |
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Notes: |
text/plain; charset=US-ASCII |
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Label: |
koch.R |
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Text: |
R program to run the models, creates Figures 3 and 4 |
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Notes: |
text/plain; charset=US-ASCII |
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kochdens.pdf |
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Text: |
Figures 4 (Koch) |
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Notes: |
application/pdf |
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kochqq.pdf |
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Text: |
Figure 3 (Koch) |
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Notes: |
application/pdf |
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matchfda.out |
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Text: |
Output from matchfda.R, for table 1 |
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Notes: |
text/plain; charset=US-ASCII |
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Label: |
matchfda.R |
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Text: |
R program to run the models and table and figure programs |
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Notes: |
text/plain; charset=US-ASCII |
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Label: |
matchp.pdf |
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Text: |
Article related to this study: Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference |
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Notes: |
application/pdf |
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Label: |
olspanel-sept06.pdf |
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Text: |
Figure 1 |
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Notes: |
application/pdf |
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Label: |
readme.txt |
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Text: |
Detailed description of data and documentation in this study |
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Notes: |
text/plain; charset=US-ASCII |
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Label: |
Visibility-Koch.csv |
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Text: |
Koch's data, read by koch.R; selected variables from full data set |
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Notes: |
text/plain; charset=US-ASCII |