Social Science Statistics Blog

3 November 2009

Airoldi on "A statistical perspective on complex networks"

I hope you can join us at the Applied Statistics Workshop this Wednesday, November 4th, when we will be happy to have Edo Airoldi, Assistant Professor in the Department of Statistics here at Harvard. Edo will be presenting a talk entitled "A statistical perspective on complex networks" for which he has provided the following abstract:

Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of science, as many scientific inquiries involve collections of measurements on pairs of objects. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. In this talk, I will review a few ideas that are central to this burgeoning literature. I will emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. I will conclude by describing open problems and challenges for machine learning and statistics.

The Applied Statistics workshop meets each Wednesday in room K-354, CGIS-Knafel (1737 Cambridge St). We start at 12 noon with a light lunch, with presentations beginning around 12:15 and we usually wrap up around 1:30 pm. We hope you can make it.

Posted by Matt Blackwell at 10:47 AM | Comments (3)

30 October 2009

Happy Halloween

It made my day when this showed up in my inbox this morning. I'm glad to see someone knows what to do if/when the zombie outbreak occurs.

Posted by Richard Nielsen at 10:05 AM | Comments (4)

29 October 2009

Matching Markets

Rich's post on instruments the other day reminded me of a conversation that I've been having with a faculty member; although the connection may not be particularly clear, at least at first.

The setup is that there are many markets in which buyers and sellers are distinct types of actors, for example, the market for spouses has, until recently, been such a market (although I make no claim as to which side of the market is buying and which is selling). This market, in the form of college applications, was analyzed by Gale and Shapley in a famous 1962 paper in which they proved that there was a solution to this type of matching problem.

Continue reading "Matching Markets"

Posted by Martin Andersen at 4:50 PM | Comments (1)

28 October 2009

Physics of politics

A physicist recently emailed me asking if I could help him access election data; he sent me one of his papers, which (to my astonishment) began "Most of the empirical electoral studies conducted by physicists . . .", followed by a string of citations. I had no idea physicists were studying elections! I suppose I should have known; from what my biologist friend tells me, physicists have been colonizing his field the way economists have done to much of social science. So I guess politics was next.

Reading a few articles in the "physics of politics" as a political scientist, one has the sense of observing an alternate universe. For example: a paper on the effect of election results on party membership in Germany that has no references to work outside of physics; features many exotic (to me at least) terms like Wegscheider potentials, the Sznajd model, and the Kronecker symbol; and takes a time-series approach to causation that I suspect would be unacceptable to most reviewers in political science and economics these days.

In general, it's clear that physicists doing work on political phenomena (or "sociophysics" more generally) are primarily interested in exploring the individual-level social interactions that might underpin the macro-order we observe in, e.g., regularities in turnout or vote share distributions. As such, political institutions (which are the major preoccupation of political scientists) necessarily disappear from the model and are typically not even mentioned, even when they would seem to be of first-order importance in explaining a particular phenomenon. (Another example of the alternate universe: a paper that argues that party vote shares in Indonesia follow a power law, but which does not describe or mention the electoral system.) These omissions seem foolish on first reading, but it's clear that they reflect a different choice of explanatory variable: physicists seek their explanations in micro-interactions, and we seek them primarily in political institutions. It's probably both of course, but models can only be so complex.

Despite my overall sense of disorientation in reading these papers, there were also somewhat surprising moments of familiarity. Physics heavily influenced economics in an earlier period of colonization, and much of what we read in economics and political science descended from those models. In reading these newer physics papers, there is therefore a sense of distant kinship, the knowledge of a common ancestor several generations back.

I wonder about the scope for collaboration between physicists and social scientists. Based on my admittedly very cursory reading of one area in which physicists have ventured, it's hard to know whether the potential gains from trade are sufficient to overcome the apparent difference in goals. For all I know there already is a lot of productive collaboration going on -- if you know of something interesting, share it in the comments!

Posted by Andy Eggers at 6:58 AM | Comments (1)

26 October 2009

Tchetgen on "Doubly robust estimation in a semi-parametric odds ratio model"

This Wednesday, October 28th, the Applied Statistics workshop will welcome Eric Tchetgen Tchetgen, Assistant Professor of Epidemiology at Harvard School of Public Health, presenting his work titled "Doubly robust estimation in a semi-parametric odds ratio model." Eric has provided the following abstract for the paper:

We consider the doubly robust estimation of the parameters in a semi-parametric conditional odds ratio model characterizing the effect of an exposure in the presence of many confounders. We develop estimators that are consistent and asymptotically normal in a union model where either a prospective baseline density function or a retrospective baseline density function is correctly specified but not necessarily both. The case of a binary outcome is of particular interest, then our approach yields a doubly robust locally efficient estimator in a semi-parametric logistic regression model For general types of outcomes, we provide a strategy to obtain doubly robust estimators that are nearly locally efficient We illustrate the method in a simulation study and an application in statistical genetics. Finally, we briefly discuss extensions of the proposed method to the semi-parametric estimation of a parameter indexing an interaction between two exposures on the logistic scale, as well as extensions to the setting of a time-varying exposure in the presence of time-varying confounding.

The Applied Statistics workshop meets each Wednesday in room K-354, CGIS-Knafel (1737 Cambridge St). We start at 12 noon with a light lunch, with presentations beginning around 12:15 and we usually wrap up around 1:30 pm. We hope you can make it.

Posted by Matt Blackwell at 11:10 AM | Comments (0)

23 October 2009

Sources of Randomness

During a recent conversation with some colleagues regarding data sources, an interesting point was made that left me pondering. One member of our group stated that he would not trust a particular source of data to provide useful estimates of population means, but he would trust it to estimate regression coefficients. This puzzled me, because a regression coefficient is a (perhaps slightly fancy) version of a mean. Why, then, would a data source that cannot be trusted for a simple average be useful for a coefficient?

Continue reading "Sources of Randomness"

Posted by Deirdre Bloome at 5:20 PM | Comments (5)

21 October 2009

Multiple Instruments

I recently found a paper by Angus Deaton that attempts to (1) discount the usefulness of instrumental variables for making causal inferences in development economics and (2) discount the usefulness of field experiments. He has definitely stirred the pot a little and is now part of an interesting debate, although the discussion seems to be more focused on Deaton's controversial claims about experiments.

Continue reading "Multiple Instruments"

Posted by Richard Nielsen at 12:41 PM | Comments (2)

20 October 2009

Elements of Statistical Learning (Online)

In case you had not already heard, Trevor Hastie, Robert Tibshirani, and Jerome Friedman have put a PDF copy of the second edition of their excellent text Elements of Statistical Learning on the book's website. I am sure many of you already own it, but a searchable version for the laptop is incredibly useful. The second edition has a lot of new content, including completely new chapters on Random Forests, Ensemble Learning, Undirected Graphical Models, and High-Dimensional Problems.

While a copy on your computer is very handy, a desk copy of this book is essential if you are interested in machine learning or data mining. The book is also a sight to behold. You can buy a copy at Amazon or Springer.

Posted by Matt Blackwell at 10:15 AM | Comments (0)

19 October 2009

Eggers on "Electoral Rules, Opposition Scrutiny, and Policy Moderation in French Municipalities"

Please join us this Wednesday October 21st when we will have a change in the schedule. We are happy to have Andy Eggers (Department of Government) presenting a talk titled "Electoral Rules, Opposition Scrutiny, and Policy Moderation in French Municipalities: An Application of the Regression Discontinuity Design." Andy has provided the following abstract for his talk:

Regression discontinuity design (RDD) is a powerful and increasingly popular approach to causal inference that can be applied when treatment is assigned deterministically based on a continuous covariate. In this talk, I will present an application of RDD from French municipalities, where the system of electing the municipal council depends on whether the city's population is above or below 3500. First I show that cities above the population cutoff have fewer uncontested elections and more opposition representation on municipal councils, consistent with expectations. I then trace the effect of these political changes -- which amount to a heightening of the scrutiny imposed on the mayor -- on policy outcomes, providing evidence that more opposition scrutiny leads to more moderate policy.

The Applied Statistics workshop meets each Wednesday in room K-354, CGIS-Knafel (1737 Cambridge St). We start at 12 noon with a light lunch, with presentations beginning around 12:15 and we usually wrap up around 1:30 pm. We hope you can make it.

Posted by Matt Blackwell at 7:21 PM | Comments (0)

14 October 2009

The Fundamental Regret of Causal Inference

Tim Kreider at the New York Times has a short piece on what he dubs "The Referendum" and how it plagues us:

The Referendum is a phenomenon typical of (but not limited to) midlife, whereby people, increasingly aware of the finiteness of their time in the world, the limitations placed on them by their choices so far, and the narrowing options remaining to them, start judging their peers' differing choices with reactions ranging from envy to contempt. ...Friends who seemed pretty much indistinguishable from you in your 20s make different choices about family or career, and after a decade or two these initial differences yield such radically divergent trajectories that when you get together again you can only regard each other's lives with bemused incomprehension.

Those familiar with casual inference will recognize this as stemming from the Fundamental Problem of Causal Inference: we cannot observe, for one individual, both their response to treatment and control. The article is an elegant look at how we grow to worry about those mysterious missing potential outcomes--the paths we didn't choose--and how we use our friends' lives to impute those missing missing outcomes. Kreider goes on to make this point exactly, with a beautiful quote from a novel:

The problem is, we only get one chance at this, with no do-overs. Life is, in effect, a non-repeatable experiment with no control. In his novel about marriage, "Light Years," James Salter writes: "For whatever we do, even whatever we do not do prevents us from doing its opposite. Acts demolish their alternatives, that is the paradox." Watching our peers' lives is the closest we can come to a glimpse of the parallel universes in which we didn't ruin that relationship years ago, or got that job we applied for, or got on that plane after all. It's tempting to read other people's lives as cautionary fables or repudiations of our own.

Perhaps the only response is that, while so close to us in so many respects, friends may be poor matches for gauging these kinds of effects. In any case, "Acts demolish their alternatives, that is the paradox" is the best description of the problem of causal inference that I have seen.

Posted by Matt Blackwell at 4:19 PM | Comments (0)