Beyond prediction: Using big data for policy problems

By Susan Athey. 2017

538 citations recorded by the source corpus.

Subfields: Epidemiology, General Social Sciences, Management Science and Operations Research, Economics & Operations Research, Social Sciences & Humanities

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Citations in the corpus

Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020, background): “, 2020), economics (Mullainathan and Spiess, 2017; Athey, 2017), public policy (Kleinberg et al.”
Racial Bias in Hate Speech and Abusive Language Detection Datasets (2019, background): “Scholars and practitioners have recently been de-voting more attention to bias in machine learning models, particularly as these models are becoming involved in more and more consequential decisions ( Athey, 2017).”
Machine Learning Methods That Economists Should Know About (2019, methodology): “, Athey and Imbens [2016], Wager and Athey [2017]) and orthogonalization (e.”
Explainable machine learning in deployment (2019, background): “The relationship between causality and explainability in MLrelatestoabroaderdichotomy.IndistinguishingMLtechniquesfrom those in econometrics or statistics, a key component is prediction vs. inference [10, 11, 33]. ML focuses on prediction, such that understandingtheunderlyingprocessisgenerallyconsid”
Explaining machine learning classifiers through diverse counterfactual explanations (2019, background): “ument is valid for a range of scenarios involving decision-making on an individual’s outcome, such as deciding admission to a university [40], screening job applicants [33], disbursing government aid [3, 5], and identifying people at high risk of a future disease [11]. In all these cases, knowing re”
Quasi-oracle estimation of heterogeneous treatment effects (2017, background): “Chernozhukov et al. (2017) discusses how Robinson’s approach to the partially linear model generalizes naturally to this case, and Athey, Tibshirani, and Wager (2018) adapt their causal forest to work with instruments.”
Poverty from Space: Using High Resolution Satellite Imagery for Estimating Economic Well-being (2017, background): “These findings raise a host of questions for further work, and contribute to an ongoing discussion regarding the use of predictive methods in public policy (Athey, 2017).”
Orthogonal ML for Demand Estimation: High Dimensional Causal Inference in Dynamic Panels∗ (2018, background): “In response, there has been growing interest in the use of Machine Learning (ML) tools to automate and accelerate the economist’s model selection process [Athey, 2017].”
Estimation and Inference about Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels. (2017, background): “on is highly subjective and labor intensive. In response, there has been growing interest in the use of Machine Learning (ML) tools to automate and accelerate the economist’s model selection process (Athey, 2017). The challenge here is to build estimators that can leverage ML prediction tools while ”
A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources (2019, background): “An additional important issue frequently neglected is that causal inference is different from prediction, although there is increasing research regarding causal inference, interpretability, and reliability of machine learning methods [55].”
Assessing the regulatory challenges of emerging disruptive technologies (2021, methodology): “The recent wave of disruptive technologies have been affected by the development of various decision technologies such as Crowdsourcing and Big Data Analytics (Prpi c et al. 2015; Athey 2017; Taeihagh 2017b) that create both new problems and opportunities for governments.”
Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities (2020, background): “With regards to external process sensing, BDACs can contribute toward identifying bottlenecks or other potential hazards in supply chains [87], detect market disturbances and monitor the financial environment [88,89], and help predict prices and availability of key resources for production [90,91].”
Offline Multi-Action Policy Learning: Generalization and Optimization (2018, methodology): “Both of these assumptions are standard and commonly adopted in both the estimation literature (Rosenbaum and Rubin (1983), Imbens (2004), Imbens and Rubin (2015), Athey et al. (2016a)) and the existing offline policy learning literature (Zhang et al. (2012), Zhao et al.”
The Impact of Machine Learning on Economics (2018, methodology): “For example, if an author has run a field experiment, there is no reason not to search for heterogeneous treatment effects using methods such as those in Athey and Imbens (2016).”