February 2007
Sun Mon Tue Wed Thu Fri Sat
        1 2 3
4 5 6 7 8 9 10
11 12 13 14 15 16 17
18 19 20 21 22 23 24
25 26 27 28      

Authors' Committee


Matt Blackwell (Gov)


Martin Andersen (HealthPol)
Kevin Bartz (Stats)
Deirdre Bloome (Social Policy)
John Graves (HealthPol)
Rich Nielsen (Gov)
Maya Sen (Gov)
Gary King (Gov)

Weekly Research Workshop Sponsors

Alberto Abadie, Lee Fleming, Adam Glynn, Guido Imbens, Gary King, Arthur Spirling, Jamie Robins, Don Rubin, Chris Winship

Weekly Workshop Schedule

Recent Comments

Recent Entries



SMR Blog
Brad DeLong
Cognitive Daily
Complexity & Social Networks
Developing Intelligence
The Education Wonks
Empirical Legal Studies
Free Exchange
Health Care Economist
Junk Charts
Language Log
Law & Econ Prof Blog
Machine Learning (Theory)
Marginal Revolution
Mixing Memory
Mystery Pollster
New Economist
Political Arithmetik
Political Science Methods
Pure Pedantry
Science & Law Blog
Simon Jackman
Social Science++
Statistical modeling, causal inference, and social science



Powered by
Movable Type 4.24-en

« February 6, 2007 | Main | February 9, 2007 »

7 February 2007

Timing Is Everything

Jim Greiner

Per previous blog posts, I'm giving today's presentation at CGIS on causal inference and immutable characteristics. I've previewed some of the ideas from this research in blog posts. Basically, the idea is that if we shift our thinking from "actual" immutable characteristics (e.g., race), a concept I find poorly defined in some situations, to perceived immutable characteristics, then the potential outcomes framework of causation can sometimes be usefully applied to things like race, gender, and ethnicity.

A key point here is the timing of treatment assignment. If treatment is conceptualized in terms of perceptions, then a natural point at which to consider treatment applied is the moment the decision maker whose conduct is being studied first perceives a unit's race, gender, ethnicity, whatever. This works well only if we're willing to exonerate the decision maker from responsibility for whatever happened before that moment of first perception. In the law, sometimes we're willing to do so. Sometimes, we're not.

Take the employment discrimination context. Typically, we don't hold an employer responsible for the discrimination of someone else, particular when it occurred (say) prior to a job application, even if that prior discrimination means that some groups (e.g., minorities) have less attractive covariates (e.g., educational achievement levels) than others (e.g., whites). Perhaps potential outcomes could work here; a study of the employer's hiring can safely condition on educational achievement levels (i.e., take them as given, balance on them, etc.) and other covariates. More covariates means that the ignorability assumption required for most causal inference is more plausible.

Contrast the employment discrimination setting to certain standards applying to education institutions. For example, we may not want to allow a university to justify allocating fewer resources to female sports teams on the grounds that its female students show less interest in sports (even if we believed the university to be telling the truth). Here, we might consider that the preferences of the female students were probably shaped by prior stereotyping, and we might want to force the university to take steps to combat those stereotypes and change the female students' preferences. If so, we are unwilling to take the previous social pressure as "given," so we cannot balance on it. The result is fewer covariates and greater pressure on the ignorability assumption.

My thanks to Professor Roderick Hills of NYU law school, whose insightful question during a job talk I recently gave there helped solidify the above Title IX example.

Posted by James Greiner at 4:00 PM