Matrix Completion Methods for Causal Panel Data Models

Athey, Susan; Bayati, Mohsen; Doudchenko, Nikolay; Imbens, Guido W.; Khosravi, Khashayar

doi:10.1080/01621459.2021.1891924

Cited by 244 publications

(314 citation statements)

References 49 publications

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“…To construct such a counterfactual, we use the pretreatment data from Merseyside and the control group data. Our main analysis uses the matrix completion method of Athey et al (2021), as implemented in the R package gsynth (Xu and Liu 2018). This method imputes the unobserved outcomes in the posttreatment period by first looking for structure in the pretreatment control data that generates good predictions of the treated unit's outcomes in the pretreatment period.…”

Section: Data and Research Designmentioning

confidence: 99%

Can Exposure to Celebrities Reduce Prejudice? The Effect of Mohamed Salah on Islamophobic Behaviors and Attitudes

et al. 2021

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Can exposure to celebrities from stigmatized groups reduce prejudice? To address this question, we study the case of Mohamed Salah, a visibly Muslim, elite soccer player. Using data on hate crime reports throughout England and 15 million tweets from British soccer fans, we find that after Salah joined Liverpool F.C., hate crimes in the Liverpool area dropped by 16% compared with a synthetic control, and Liverpool F.C. fans halved their rates of posting anti-Muslim tweets relative to fans of other top-flight clubs. An original survey experiment suggests that the salience of Salah’s Muslim identity enabled positive feelings toward Salah to generalize to Muslims more broadly. Our findings provide support for the parasocial contact hypothesis—indicating that positive exposure to out-group celebrities can spark real-world behavioral changes in prejudice.

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Section: Data and Research Designmentioning

confidence: 99%

Can Exposure to Celebrities Reduce Prejudice? The Effect of Mohamed Salah on Islamophobic Behaviors and Attitudes

et al. 2021

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“…We explore product differentiation (HDD, SSD, Flash drives) to find our preferred Control group, SSD products and SSD manufacturing firms. To eliminate issues reegarding pre-merger parallel trend, our preferred method would be to use a synthetic control group, as described in Abadie and Gardeazabal (2003), Abadie, Diamond, and Hainmueller (2010), and Abadie et al (2015), making use of the matrix completion extension presented in Athey, Bayati, Doudchenko, Imbens, and Khosravi (2018). The idea behind this method is to create a weighted sample of firms that are most similar to the Treatment firm based on a set of observable characteristics.…”

Section: Ssd/flash As Control Groupmentioning

confidence: 99%

Mergers and innovation: Evidence from the hard disk drive market

Bennato

Davies

Mariuzzo

et al. 2021

International Journal of Industrial Organization

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This case study is a relatively rare ex-post evaluation of how the level of innovation changed after the 5-to-3 consolidation of the world-wide hard disk drive (HDD) industry. We take a holistic view of innovation, employing four different measures: R&D expenditure and patent activity as indicators of innovative inputs, and the number of new products marketed, and their unit user costs as indicators of innovative output. This allows us to distinguish the magnitude of the merging parties' innovative efforts from the productivity of those efforts.Of the remaining HDD manufacturers, for Seagate we found an increase in all our innovation measures following the mergers, but for Western Digital the evidence is mixed.Methodologically, the paper draws light on some of the challenges of conducting similar case-specific retrospective studies on the impact of mergers on innovation.

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“…The main contribution of [18] was to estimate the effect of the studied wildfires over time.The wildfire effect was estimated as the difference between the observed spectral index and the estimated counterfactual (the values that the spectral index would have taken in a hypothetical scenario with the absence of wildfire). Counterfactuals are estimated in [18] following the proposals in [31], a way to perform GSC [23] based on matrix completion.…”

Section: Effects Of Wildfires Datamentioning

confidence: 99%

Wildfires Vegetation Recovery through Satellite Remote Sensing and Functional Data Analysis

2021

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In recent years, wildfires have caused havoc across the world, which are especially aggravated in certain regions due to climate change. Remote sensing has become a powerful tool for monitoring fires, as well as for measuring their effects on vegetation over the following years. We aim to explain the dynamics of wildfires’ effects on a vegetation index (previously estimated by causal inference through synthetic controls) from pre-wildfire available information (mainly proceeding from satellites). For this purpose, we use regression models from Functional Data Analysis, where wildfire effects are considered functional responses, depending on elapsed time after each wildfire, while pre-wildfire information acts as scalar covariates. Our main findings show that vegetation recovery after wildfires is a slow process, affected by many pre-wildfire conditions, among which the richness and diversity of vegetation is one of the best predictors for the recovery.

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Matrix Completion Methods for Causal Panel Data Models

Cited by 244 publications

References 49 publications

Can Exposure to Celebrities Reduce Prejudice? The Effect of Mohamed Salah on Islamophobic Behaviors and Attitudes

Can Exposure to Celebrities Reduce Prejudice? The Effect of Mohamed Salah on Islamophobic Behaviors and Attitudes

Mergers and innovation: Evidence from the hard disk drive market

Wildfires Vegetation Recovery through Satellite Remote Sensing and Functional Data Analysis

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