# Social Science Statistics Blog

September 2012
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 | 29
30 |  |  |  |  |  |

### Authors' Committee

#### Chair:

Matt Blackwell (Gov)

#### Members:

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

Search this site:

### Recent Comments

- Re: App Stats: Miratrix on "Random Weight Estimators: Adjusting Randomized Trials Without Using Observed Outcomes"
  By: azaz
- Re: App Stats: Miratrix on "Random Weight Estimators: Adjusting Randomized Trials Without Using Observed Outcomes"
  By: azaz
- Re: App Stats: Scanlan on "The Mismeasure of Group Differences in the Law and the Social and Medical Sciences"
  By: sakib
- Re: App Stats: Teixeira on "Viral Video Advertising"
  By: huijj002
- Re: App Stats: Teixeira on "Viral Video Advertising"
  By: Bery C.P
- Re: App Stats: Teixeira on "Viral Video Advertising"
  By: www.announceamerica.com
- Re: App Stats: Miratrix on "Random Weight Estimators: Adjusting Randomized Trials Without Using Observed Outcomes"
  By: zetar
- Re: App Stats: Miratrix on "Random Weight Estimators: Adjusting Randomized Trials Without Using Observed Outcomes"
  By: gutscheinezumausdrucken
- Re: App Stats: Miratrix on "Random Weight Estimators: Adjusting Randomized Trials Without Using Observed Outcomes"
  By: goga7777
- Re: App Stats: Grubb on "Cellular Service Demand: Biased Beliefs, Learning, and Bill Shock"
  By: luka

### Recent Entries

- App Stats: Kasy on "Identification in General Triangular Systems"
- App Stats: Miratrix on "Random Weight Estimators: Adjusting Randomized Trials Without Using Observed Outcomes"
- App Stats: Nielsen on "Jihadi Radicalization of Muslim Clerics"
- Pop music is getting louder, possibly more boring (statistically speaking)
- App Stats: Robins on "A Simple Unification of the Potential Outcome and Causal Graph Approaches to Causal Inference"
- App Stats: Grubb on "Cellular Service Demand: Biased Beliefs, Learning, and Bill Shock"

### Categories

- Regular Post

### Blogroll

SMR Blog
Brad DeLong
Cognitive Daily
Complexity & Social Networks
Developing Intelligence
EconLog
The Education Wonks
Empirical Legal Studies
Free Exchange
Freakonomics
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

### Archives

- November 2012
- October 2012
- September 2012
- July 2012
- June 2012
- May 2012
- April 2012
- March 2012
- February 2012
- January 2012
- November 2011
- October 2011
- September 2011
- July 2011
- June 2011
- May 2011
- April 2011
- March 2011
- February 2011
- January 2011
- December 2010
- November 2010
- October 2010
- September 2010
- August 2010
- May 2010
- April 2010
- March 2010
- February 2010
- January 2010
- December 2009
- November 2009
- October 2009
- September 2009
- August 2009
- June 2009
- May 2009
- April 2009
- March 2009
- February 2009
- January 2009
- December 2008
- November 2008
- October 2008
- September 2008
- August 2008
- June 2008
- May 2008
- April 2008
- March 2008
- February 2008
- January 2008
- December 2007
- November 2007
- October 2007
- September 2007
- June 2007
- May 2007
- April 2007
- March 2007
- February 2007
- January 2007
- December 2006
- November 2006
- October 2006
- September 2006
- August 2006
- July 2006
- June 2006
- May 2006
- April 2006
- March 2006
- February 2006
- January 2006
- December 2005
- November 2005
- October 2005
- September 2005

### Notification

Enter e-mail to [un]subscribe:
Powered by
Movable Type 4.24-en
Syndicate this site (XML)
[an error occurred while processing this directive]

« July 2012 | Main | October 2012 »

30 September 2012

### App Stats: Kasy on "Identification in General Triangular Systems"

We hope you can join us this Wednesday, October 3, 2012 for the Applied Statistics Workshop. Maximilian Kasy, Assistant Professor of Economics from the Department of Economics at Harvard University, will give a presentation entitled "Identification in General Triangular Systems". A light lunch will be served at 12 pm and the talk will begin at 12.15.

"Identification in General Triangular Systems"
Maximilian Kasy
Department of Economics, Harvard University
CGIS K354 (1737 Cambridge St.)
Wednesday, October 3rd, 2012 12.00 pm

Abstract:

> This paper discusses identification in continuous triangular systems without restrictions on heterogeneity or functional form. In particular, we do not assume separability of structural functions, restrictions on the dimensionality of unobservables, or monotonicity in unobservables. We do maintain monotonicity of the first stage relationship in the instrument. We show that under this condition alone, and given rich enough support of the data, we can achieve point identification of potential outcome distributions, and in particular of the average structural function. If the support of the continuous instrument is not large enough potential outcome distributions are partially identified. If the instrument is discrete identification fails completely. The setup discussed in this paper covers important cases not covered by existing approaches such as conditional moment restrictions (c.f. Newey and Powell, 2003) and control variables (c.f. Imbens and Newey, 2009). It covers, in particular, random coefficient models, as well as models arising as the reduced form of a system of structural equations.

Posted by Konstantin Kashin at 11:28 PM | Comments (0)

24 September 2012

### App Stats: Miratrix on "Random Weight Estimators: Adjusting Randomized Trials Without Using Observed Outcomes"

We hope you can join us this Wednesday, September 26, 2012 for the Applied Statistics Workshop. Luke Miratrix, Assistant Professor of Statistics in the Department of Statistics at Harvard University, will give a presentation entitled "Random Weight Estimators: Adjusting Randomized Trials Without Using Observed Outcomes". A light lunch will be served at 12 pm and the talk will begin at 12.15.

"Random Weight Estimators: Adjusting Randomized Trials Without Using Observed Outcomes"
Luke Miratrix
Department of Statistics, Harvard University
CGIS K354 (1737 Cambridge St.)
Wednesday, September 26th, 2012 12.00 pm

Abstract:

> To increase the precision of a randomized trial, experimenters often adjust estimates of treatment effects using baseline covariates thought to predict the outcome of interest. In a previous paper, we proved that even under the Neyman-Rubin model, if the covariates and the method for adjustment are determined before randomization, this process can increase precision in a manner quite similar to a comparable blocked experiment. Typically, however, experimenters wish to adjust for the covariates that are most imbalanced between treatment and control, given the realized randomization. This leads to a much vexed variable selection problem that depends on the observed treatment assignment. To understand the issues behind this process, we examine a class of estimators we call "Random Weight Estimators" that adjust treatment effect estimates by weighting units with weights depending on a function on treatment assignment and covariates. While similar in spirit to blocking, these estimators can be applied "after the fact,'' i.e., after randomization has occurred, allowing them to naturally adapt to the observed treatment assignment. They can also adjust for many different covariates at once, including continuous ones. This class is quite general, and it includes traditional methods such as ordinary linear regression. Using our framework, we show, under the Neyman-Rubin model, how one can easily introduce potential bias using what would seem to be legitimate and simple approaches, especially in small and midsize experiments. Care must be taken with many forms of adjustment, even if an approach is selected without regard to any actual outcomes. We also extend this methodology to survey experiments, giving an appropriate and near-unbiased estimator for the treatment effect of a parent population. Throughout the talk, we illustrate this overall framework.

Posted by Konstantin Kashin at 11:40 AM | Comments (5)

16 September 2012

### App Stats: Nielsen on "Jihadi Radicalization of Muslim Clerics"

We hope you can join us this Wednesday, September 19, 2012 for the Applied Statistics Workshop. Rich Nielsen, a Ph.D. candidate from the Department of Government at Harvard University, will give a practice job talk entitled "Jihadi Radicalization of Muslim Clerics". A light lunch will be served at 12 pm and the talk will begin at 12.15.

"Jihadi Radicalization of Muslim Clerics"
Rich Nielsen
Government Department, Harvard University
CGIS K354 (1737 Cambridge St.)
Wednesday, September 19th, 2012 12.00 pm

Abstract:

> This paper explains why some Muslim clerics adopt the ideology of militant Jihad while others do not. I argue that clerics strategically adopt or reject Jihadi ideology because of career incentives generated by the structure of cleric educational networks. Well-connected clerics enjoy substantial success at pursuing comfortable careers within state-run religious institutions and they reject Jihadi ideology in exchange for continued material support from the state. Clerics with poor educational networks cannot rely on connections to advance through the state-run institutions, so many pursue careers outside of the system by appealing directly to lay audiences for support. These clerics are more likely to adopt Jihadi ideology because it helps them demonstrate to potential supporters that they have not been theologically coopted by political elites. I provide evidence of these dynamics by collecting and analyzing 29,430 fatwas, articles, and books written by 91 contemporary clerics. Using statistical natural language processing, I measure the extent to which each cleric adopts Jihadi ideology in their writing. I combine this with biographical and network information about each cleric to trace the process by which poorly-connected clerics become more likely to adopt Jihadi ideology.

Posted by Konstantin Kashin at 10:37 PM | Comments (0)

### Pop music is getting louder, possibly more boring (statistically speaking)

The NYT just alerted me to a paper by Joan Serra and coauthors demonstrating what we can learn about popular music with a big data approach. I'll leave it to you to interpret the trends they identify (music is getting louder, also more similar), but it was interesting and gave me a lot of ideas for how I could borrow some of this technology for my own research.

Posted by Richard Nielsen at 8:51 PM | Comments (0)

9 September 2012

### App Stats: Robins on "A Simple Unification of the Potential Outcome and Causal Graph Approaches to Causal Inference"

We hope you can join us this Wednesday, September 12, 2012 for the Applied Statistics Workshop. Jamie Robins, Professor of Epidemiology from the Harvard School of Public Health, will give a presentation entitled "A Simple Unification of the Potential Outcome and Causal Graph Approaches to Causal Inference". A light lunch will be served at 12 pm and the talk will begin at 12.15.

"A Simple Unification of the Potential Outcome and Causal Graph Approaches to Causal Inference"
Jamie Robins
Harvard School of Public Health
CGIS K354 (1737 Cambridge St.)
Wednesday, September 12th, 2012 12.00 pm

Abstract:

> Potential outcomes are extensively used within statistics, epidemiology, and political science for reasoning about causation. Directed acyclic graphs are another formalism used to represent causal systems. They are extensively used in computer science, bioinformatics, sociology and epidemiology. It is natural to wish to unify them.
> We present a simple approach to this unification. The approach is based on the idea of splitting nodes to construct graphs whose nodes are potential outcomes. The resulting graph can be used to read off counterfactual independencies. These independencies are satisfied by all previously proposed graphical and nongraphical causal models. We review many examples to illustrate the power of this approach.

This is joint work with Thomas Richardson at the University of Washington.

Posted by Konstantin Kashin at 5:53 PM

3 September 2012

### App Stats: Grubb on "Cellular Service Demand: Biased Beliefs, Learning, and Bill Shock"

We hope you can join us this Wednesday, September 5, 2012 for the first Applied Statistics Workshop of the Fall 2012 semester. Michael Grubb, an Assistant Professor of Applied Economics from the MIT Sloan School of Management, will give a presentation entitled "Cellular Service Demand: Biased Beliefs, Learning, and Bill Shock". A light lunch will be served at 12 pm and the talk will begin at 12.15.

"Cellular Service Demand: Biased Beliefs, Learning, and Bill Shock"
Michael Grubb
MIT Sloan School of Management
CGIS K354 (1737 Cambridge St.)
Wednesday, September 5th, 2012 12.00 pm

Abstract:

> By April 2013, the FCC's recent bill-shock agreement with cellular carriers requires consumers be notified when exceeding usage allowances. Will the agreement help or hurt consumers? To answer this question, we estimate a model of consumer plan choice, usage, and learning using a panel of cellular bills. Our model predicts that the agreement will lower average consumer welfare by $2 per year because firms will respond by raising monthly fees. Our approach is based on novel evidence that consumers are inattentive to past usage (meaning that bill-shock alerts are informative) and advances structural modeling of demand in situations where multi-part tariffs induce marginal-price uncertainty. Additionally, our model estimates show that an average consumer underestimates both the mean and variance of future calling. These biases cost consumers $42 per year at existing prices. Moreover, absent bias, the bill-shock agreement would have little to no effect.

Posted by Konstantin Kashin at 3:02 PM