Publications
2018
Sharma, Amit; Hofman, Jake M.; Watts, Duncan J.
Split-Door Criterion: Identification of Causal Effects Through Auxiliary Outcomes Journal Article
In: The Annals of Applied Statistics, vol. 12, no. 4, pp. 2699-2733, 2018.
@article{sharma2018split,
title = {Split-Door Criterion: Identification of Causal Effects Through Auxiliary Outcomes},
author = {Amit Sharma and Jake M. Hofman and Duncan J. Watts},
url = {https://drive.google.com/file/d/1SlOC1sdKPM4FZAceDjHTaOj5GVf3f5F5/view?usp=sharing},
doi = {10.1214/18-AOAS1179},
year = {2018},
date = {2018-04-01},
urldate = {2018-04-01},
journal = {The Annals of Applied Statistics},
volume = {12},
number = {4},
pages = {2699-2733},
abstract = {We present a method for estimating causal effects in time series data when fine-grained information about the outcome of interest is available. Specifically, we examine what we call the split-door setting, where the outcome variable can be split into two parts: one that is potentially affected by the cause being studied and another that is independent of it, with both parts sharing the same (unobserved) confounders. We show that under these conditions, the problem of identification reduces to that of testing for independence among observed variables, and propose a method that uses this approach to automatically find subsets of the data that are causally identified. We demonstrate the method by estimating the causal impact of Amazon’s recommender system on traffic to product pages, finding thousands of examples within the dataset that satisfy the split-door criterion. Unlike past studies based on natural experiments that were limited to a single product category, our method applies to a large and representative sample of products viewed on the site. In line with previous work, we find that the widely-used click-through rate (CTR) metric overestimates the causal impact of recommender systems; depending on the product category, we estimate that 50–80% of the traffic attributed to recommender systems would have happened even without any recommendations. We conclude with guidelines for using the split-door criterion as well as a discussion of other contexts where the method can be applied.},
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We present a method for estimating causal effects in time series data when fine-grained information about the outcome of interest is available. Specifically, we examine what we call the split-door setting, where the outcome variable can be split into two parts: one that is potentially affected by the cause being studied and another that is independent of it, with both parts sharing the same (unobserved) confounders. We show that under these conditions, the problem of identification reduces to that of testing for independence among observed variables, and propose a method that uses this approach to automatically find subsets of the data that are causally identified. We demonstrate the method by estimating the causal impact of Amazon’s recommender system on traffic to product pages, finding thousands of examples within the dataset that satisfy the split-door criterion. Unlike past studies based on natural experiments that were limited to a single product category, our method applies to a large and representative sample of products viewed on the site. In line with previous work, we find that the widely-used click-through rate (CTR) metric overestimates the causal impact of recommender systems; depending on the product category, we estimate that 50–80% of the traffic attributed to recommender systems would have happened even without any recommendations. We conclude with guidelines for using the split-door criterion as well as a discussion of other contexts where the method can be applied.
Lazer, David M. J.; Baum, Matthew A.; Benkler, Yochai; Berinsky, Adam J.; Greenhill, Kelly M.; Menczer, Filippo; Metzger, Miriam J.; Nyhan, Brendan; Pennycook, Gordon; Rothschild, David; Schudson, Michael; Sloman, Steven A.; Sunstein, Cass R.; Thorson, Emily A.; Watts, Duncan J.; Zittrain, Jonathan L.
The science of fake news Journal Article
In: Science, vol. 359, no. 6380, pp. 1094-1096, 2018.
@article{lazer2018the,
title = {The science of fake news},
author = {David M. J. Lazer and Matthew A. Baum and Yochai Benkler and Adam J. Berinsky and Kelly M. Greenhill and Filippo Menczer and Miriam J. Metzger and Brendan Nyhan and Gordon Pennycook and David Rothschild and Michael Schudson and Steven A. Sloman and Cass R. Sunstein and Emily A. Thorson and Duncan J. Watts and Jonathan L. Zittrain},
url = {https://science.sciencemag.org/content/359/6380/1094/tab-pdf},
doi = {10.1126/science.aao2998},
year = {2018},
date = {2018-03-09},
journal = {Science},
volume = {359},
number = {6380},
pages = {1094-1096},
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Benjamin, Daniel J.
Redefine statistical significance Journal Article
In: Nature Human Behavior, vol. 2, pp. 6-10, 2018.
@article{benjamin2018redefine,
title = {Redefine statistical significance},
author = {Daniel J. Benjamin et al.},
url = {https://drive.google.com/file/d/1JaXDX3A47agzQd1fMyFlZ5nO_4TJLxdQ/view?usp=sharing},
doi = {10.1038/s41562-017-0189-z},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Nature Human Behavior},
volume = {2},
pages = {6-10},
keywords = {},
pubstate = {published},
tppubtype = {article}
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2017
Houghton, James P.; Siegel, Michael; Madnick, Stuart; Tounaka, Nobuaki; Nakamura, Kazutaka; Sugiyama, Takaaki; Nakagawa, Daisuke; Shirnen, Buyanjargal
Beyond Keywords: Tracking the evolution of conversational clusters in social media Journal Article
In: Sociological Methods & Research, vol. 48, no. 3, pp. 588-607, 2017.
@article{houghton2017beyond,
title = {Beyond Keywords: Tracking the evolution of conversational clusters in social media},
author = {James P. Houghton and Michael Siegel and Stuart Madnick and Nobuaki Tounaka and Kazutaka Nakamura and Takaaki Sugiyama and Daisuke Nakagawa and Buyanjargal Shirnen},
url = {https://core.ac.uk/download/pdf/188187247.pdf},
doi = {10.1177/0049124117729705},
year = {2017},
date = {2017-10-09},
journal = {Sociological Methods & Research},
volume = {48},
number = {3},
pages = {588-607},
abstract = {The potential of social media to give insight into the dynamic evolution of public conversations, and into their reactive and constitutive role in political activities, has to date been underdeveloped. While topic modeling can give static insight into the structure of a conversation, and keyword volume tracking can show how engagement with a specific idea varies over time, there is need for a method of analysis able to understand how conversations about societal values evolve and react to events in the world, incorporating new ideas and relating them to existing themes. In this paper, we propose a method for analyzing social media messages that formalizes the structure of public conversations, and allows the sociologist to study the evolution of public discourse in a rigorous, replicable, and data-driven fashion. This approach may be useful to those studying the social construction of meaning, the origins of factionalism and internecine conflict, or boundary-setting and group-identification exercises; and has potential implications for those working to promote understanding and intergroup reconciliation.},
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The potential of social media to give insight into the dynamic evolution of public conversations, and into their reactive and constitutive role in political activities, has to date been underdeveloped. While topic modeling can give static insight into the structure of a conversation, and keyword volume tracking can show how engagement with a specific idea varies over time, there is need for a method of analysis able to understand how conversations about societal values evolve and react to events in the world, incorporating new ideas and relating them to existing themes. In this paper, we propose a method for analyzing social media messages that formalizes the structure of public conversations, and allows the sociologist to study the evolution of public discourse in a rigorous, replicable, and data-driven fashion. This approach may be useful to those studying the social construction of meaning, the origins of factionalism and internecine conflict, or boundary-setting and group-identification exercises; and has potential implications for those working to promote understanding and intergroup reconciliation.
Jasny, B. R.; Wigginton, N.; McNutt, M.; Bubela, T.; Buck, S.; Cook-Deegan, R.; Gardner, T.; Hanson, B.; Hustad, C.; Kiermer, V.; Lazer, D.; Lupia, A.; Manrai, A.; McConnell, L.; Noonan, K.; Phimister, E.; Simon, B.; Strandburg, K.; Summers, Z.; Watts, D.
Fostering reproducibility in industry-academia research Journal Article
In: Science, vol. 357, no. 6353, pp. 759-761, 2017.
@article{jasny2017fostering,
title = {Fostering reproducibility in industry-academia research},
author = {B. R. Jasny and N. Wigginton and M. McNutt and T. Bubela and S. Buck and R. Cook-Deegan and T. Gardner and B. Hanson and C. Hustad and V. Kiermer and D. Lazer and A. Lupia and A. Manrai and L. McConnell and K. Noonan and E. Phimister and B. Simon and K. Strandburg and Z. Summers and D. Watts},
url = {https://drive.google.com/file/d/1HtrSro1FT6cFmlImWYTs_GonbiKbi9Og/view?usp=sharing},
year = {2017},
date = {2017-08-25},
urldate = {2017-08-25},
journal = {Science},
volume = {357},
number = {6353},
pages = {759-761},
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Hofman, Jake M; Sharma, Amit; Watts, Duncan J
Prediction and explanation in social systems Journal Article
In: Science, vol. 355, no. 6324, pp. 486–488, 2017.
@article{hofman2017prediction,
title = {Prediction and explanation in social systems},
author = {Jake M Hofman and Amit Sharma and Duncan J Watts},
url = {https://drive.google.com/file/d/1lkTvMFVOBt7GZ6bu4M2nYPWb017y68XC/view},
doi = {10.1126/science.aal3856},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {Science},
volume = {355},
number = {6324},
pages = {486--488},
publisher = {American Association for the Advancement of Science},
abstract = {Historically, social scientists have sought out explanations of human and social phenomena that provide interpretable causal mechanisms, while often ignoring their predictive accuracy. We argue that the increasingly computational nature of social science is beginning to reverse this traditional bias against prediction; however, it has also highlighted three important issues that require resolution. First, current practices for evaluating predictions must be better standardized. Second, theoretical limits to predictive accuracy in complex social systems must be better characterized, thereby setting expectations for what can be predicted or explained. Third, predictive accuracy and interpretability must be recognized as complements, not substitutes, when evaluating explanations. Resolving these three issues will lead to better, more replicable, and more useful social science.},
keywords = {},
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Historically, social scientists have sought out explanations of human and social phenomena that provide interpretable causal mechanisms, while often ignoring their predictive accuracy. We argue that the increasingly computational nature of social science is beginning to reverse this traditional bias against prediction; however, it has also highlighted three important issues that require resolution. First, current practices for evaluating predictions must be better standardized. Second, theoretical limits to predictive accuracy in complex social systems must be better characterized, thereby setting expectations for what can be predicted or explained. Third, predictive accuracy and interpretability must be recognized as complements, not substitutes, when evaluating explanations. Resolving these three issues will lead to better, more replicable, and more useful social science.
Watts, Duncan J
Should social science be more solution-oriented? Journal Article
In: Nature Human Behaviour, vol. 1, no. 1, pp. 1–5, 2017.
@article{watts2017should,
title = {Should social science be more solution-oriented?},
author = {Duncan J Watts},
url = {https://drive.google.com/file/d/1gsJRHhwcwW1C-zf3PStz1CqlVILUIitB/view},
doi = {10.1038/s41562-016-0015},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {Nature Human Behaviour},
volume = {1},
number = {1},
pages = {1--5},
publisher = {Nature Publishing Group},
abstract = {Over the past 100 years, social science has generated a tremendous number of theories on the topics of individual and collective human behaviour. However, it has been much less successful at reconciling the innumerable inconsistencies and contradictions among these competing explanations, a situation that has not been resolved by recent advances in `computational social science'. In this Perspective, I argue that this `incoherency problem' has been perpetuated by an historical emphasis in social science on the advancement of theories over the solution of practical problems. I argue that one way for social science to make progress is to adopt a more solution-oriented approach, starting first with a practical problem and then asking what theories (and methods) must be brought to bear to solve it. Finally, I conclude with a few suggestions regarding the sort of problems on which progress might be made and how we might organize ourselves to solve them.},
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Over the past 100 years, social science has generated a tremendous number of theories on the topics of individual and collective human behaviour. However, it has been much less successful at reconciling the innumerable inconsistencies and contradictions among these competing explanations, a situation that has not been resolved by recent advances in `computational social science'. In this Perspective, I argue that this `incoherency problem' has been perpetuated by an historical emphasis in social science on the advancement of theories over the solution of practical problems. I argue that one way for social science to make progress is to adopt a more solution-oriented approach, starting first with a practical problem and then asking what theories (and methods) must be brought to bear to solve it. Finally, I conclude with a few suggestions regarding the sort of problems on which progress might be made and how we might organize ourselves to solve them.
2014
Watts, Duncan
Common Sense and Sociological Explanations Journal Article
In: American Journal of Sociology, vol. 120, no. 2, pp. 313-351, 2014.
@article{watts2014common,
title = {Common Sense and Sociological Explanations},
author = {Duncan Watts},
url = {http://www.jstor.org/stable/10.1086/678271},
doi = {10.1086/678271},
year = {2014},
date = {2014-09-01},
journal = {American Journal of Sociology},
volume = {120},
number = {2},
pages = {313-351},
abstract = {Sociologists have long advocated a sociological approach to explanation by contrasting it with common sense. The argument of this article, however, is that sociologists rely on common sense more than they realize. Moreover, this unacknowledged reliance causes serious problems for their explanations of social action, that is, for why people do what they do. Many such explanations, it is argued, conflate understandability with causality in ways that are not valid by the standards of scientific explanation. It follows that if sociologists want their explanations to be scientifically valid, they must evaluate them specifically on those grounds—in particular, by forcing them to make predictions. In becoming more scientific, however, it is predicted that sociologists’ explanations will also become less satisfying from an intuitive, sense-making perspective. Even as novel sources of data and improved methods open exciting new directions for sociological research, therefore, sociologists will increasingly have to choose between unsatisfying scientific explanations and satisfying but unscientific stories.},
keywords = {},
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Sociologists have long advocated a sociological approach to explanation by contrasting it with common sense. The argument of this article, however, is that sociologists rely on common sense more than they realize. Moreover, this unacknowledged reliance causes serious problems for their explanations of social action, that is, for why people do what they do. Many such explanations, it is argued, conflate understandability with causality in ways that are not valid by the standards of scientific explanation. It follows that if sociologists want their explanations to be scientifically valid, they must evaluate them specifically on those grounds—in particular, by forcing them to make predictions. In becoming more scientific, however, it is predicted that sociologists’ explanations will also become less satisfying from an intuitive, sense-making perspective. Even as novel sources of data and improved methods open exciting new directions for sociological research, therefore, sociologists will increasingly have to choose between unsatisfying scientific explanations and satisfying but unscientific stories.
2011
Watts, Duncan J.
Crown, 2011, ISBN: 9780385531696, 0385531699.
@book{watts2011everything,
title = {Everything Is Obvious},
author = {Duncan J. Watts},
url = {https://www.google.com/books/edition/Everything_Is_Obvious/kT_4AAAAQBAJ?hl=en&gbpv=0},
isbn = {9780385531696, 0385531699},
year = {2011},
date = {2011-03-29},
publisher = {Crown},
abstract = {Why is the Mona Lisa the most famous painting in the world? Why did Facebook succeed when other social networking sites failed? Did the surge in Iraq really lead to less violence? How much can CEO's impact the performance of their companies? And does higher pay incentivize people to work hard?
If you think the answers to these questions are a matter of common sense, think again. As sociologist and network science pioneer Duncan Watts explains in this provocative book, the explanations that we give for the outcomes that we observe in life--explanation that seem obvious once we know the answer--are less useful than they seem.},
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Why is the Mona Lisa the most famous painting in the world? Why did Facebook succeed when other social networking sites failed? Did the surge in Iraq really lead to less violence? How much can CEO's impact the performance of their companies? And does higher pay incentivize people to work hard?
If you think the answers to these questions are a matter of common sense, think again. As sociologist and network science pioneer Duncan Watts explains in this provocative book, the explanations that we give for the outcomes that we observe in life--explanation that seem obvious once we know the answer--are less useful than they seem.
If you think the answers to these questions are a matter of common sense, think again. As sociologist and network science pioneer Duncan Watts explains in this provocative book, the explanations that we give for the outcomes that we observe in life--explanation that seem obvious once we know the answer--are less useful than they seem.