Statistical Inference for Causal Effects

Mealli, Fabrizia; Pacini, Barbara; Rubin, Donald B.

doi:10.1002/9781119961154.ch10

Cited by 11 publications

(9 citation statements)

References 38 publications

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“…We carried out the analysis by identifying universities most affected by the hurricane as the treatment group and comparing it with similar but unaffected universities as the control group. Universities in the control group were identified using matching (45,47,(50)(51)(52)(53)(54). The data have been archived at Facebook.…”

Section: Data and Study Designmentioning

confidence: 99%

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A natural experiment of social network formation and dynamics

Phan

Airoldi

2015

Proc. Natl. Acad. Sci. U.S.A.

106

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Social networks affect many aspects of life, including the spread of diseases, the diffusion of information, the workers' productivity, and consumers' behavior. Little is known, however, about how these networks form and change. Estimating causal effects and mechanisms that drive social network formation and dynamics is challenging because of the complexity of engineering social relations in a controlled environment, endogeneity between network structure and individual characteristics, and the lack of timeresolved data about individuals' behavior. We leverage data from a sample of 1.5 million college students on Facebook, who wrote more than 630 million messages and 590 million posts over 4 years, to design a long-term natural experiment of friendship formation and social dynamics in the aftermath of a natural disaster. The analysis shows that affected individuals are more likely to strengthen interactions, while maintaining the same number of friends as unaffected individuals. Our findings suggest that the formation of social relationships may serve as a coping mechanism to deal with high-stress situations and build resilience in communities.social networks | natural disasters | causal inference | natural experiment | propensity score matching S ocial networks affect many aspects of life, including the spread of diseases (1), access to resources and information (2), the diffusion of knowledge (3, 4), productivity and stability of organizations (5, 6), and job prospects (7,8). In this paper, we conceptualize a natural experiment † by taking advantage of the well-defined local impact of a hurricane to gain a quantitative understanding of how these networks form and evolve. Our analysis provides insights into how to leverage social dynamics for affecting outcomes of interest, such as how to design policies that can aid rescue and recovery efforts or influence behavior and the economy.Establishing causal relationships in social network formation and dynamics has historically been difficult to study because of endogeneity between network structure and individual characteristics, and the cost of obtaining long time-series about individuals ' behavior (16, 17). In addition, large-scale randomized experiments are often not feasible because of the complexity of engineering social relations in a controlled environment, and the multitude of incentives that influence human behavior (18)(19)(20), and often because of privacy and IRB related issues (e.g., see refs. 21 and 22). Recent research tackle these challenges by developing behavioral models of network formation (23) that can support what-if analyses, and by using automated services such as Amazon Mechanical Turk (aws.amazon.com/documentation/ mturk) to carry out randomized human-subjects experiments of social dynamics in artificial environments, at scale (24-26). Related literature focuses on incentives, aiming at separating influence from selection effects, a task for which negative results exist in general (27, 28), using randomized experiments (29-32) and strategie...

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Section: Data and Study Designmentioning

confidence: 99%

“…From a statistical perspective, a natural experiment uses historical (observational) data as an effective means to identify causal mechanisms (44)(45)(46)(47). Natural experiments (48) further qualify a subset of observational studies where plausible experiments can be conceptualized.…”

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confidence: 99%

A natural experiment of social network formation and dynamics

Phan

Airoldi

2015

Proc. Natl. Acad. Sci. U.S.A.

106

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“…It is only possible to rely on subjectmatter knowledge. The SUTVA is a substantive assumption, which is usually maintained even though it is not always appropriate (Mealli et al 2011). demand, we could completely relax the SUTVA and develop an evaluation strategy capable of detecting the extent of each spillover. Unfortunately, this kind of information is hardly ever available, and we should settle for a less ambitious aim.…”

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confidence: 99%

Industrial policy evaluation in the presence of spillovers

Cerqua

Pellegrini

2017

Small Bus Econ

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The shortage of studies on spatial spillovers of capital subsidy policies is rather surprising, considering that such policies are usually designed to generate spatial externalities. We propose a new framework that allows positive agglomeration effects to be contrasted with the negative cross-sectional substitution and the crowding-out effect. The global evaluation of the ATT and the spillover parameters shifts the spotlight from the policy effect on subsidised firms to the global effect of capital subsidy policies on the targeted territory. The empirical evaluation of a policy in Italy mainly directed towards small-and medium-sized firms shows that the impact on investments, turnover and employment is positive and large, but is negative on TFP. However, the employment growth is partially determined to the detriment of the untreated firms.

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“…The randomization inference is non-parametric in that it does not make any functional form assumption and it is exact in that it does not rely on large sample approximations. Thus, results coming out of this analysis are exact and valid irrespective of the number of group assigned to each treatment status (e.g., Small et al, 2008, Imbens andWoolbridge, 2009;Mealli et al, 2011). An individual-level analysis which accounts for the presence of intraclass correlation, using e.g., mixed effect regression models, may lead to more powerful model-based tests if the model is well specified than group randomization inference (Braun and Feng, 2001).…”

Section: The Evaluation Of the Innovare Programmentioning

confidence: 95%