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Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are inconsistent. To illustrate the severity of this issue, we randomly generate placebo laws in state-level data on female wages from the Current Population Survey. For each law, we use OLS to compute the DD estimate of its “effect” as well as the standard error of this estimate. These conventional DD standard errors severely understate the standard deviation of the estimators: we find an “effect” significant at the 5 percent level for up to 45 percent of the placebo interventions. We use Monte Carlo simulations to investigate how well existing methods help solve this problem. Econometric corrections that place a specific parametric form on the time-series process do not perform well. Bootstrap (taking into account the autocorrelation of the data) works well when the number of states is large enough. Two corrections based on asymptotic approximation of the variance-covariance matrix work well for moderate numbers of states and one correction that collapses the time series information into a “pre”- and “post”-period and explicitly takes into account the effective sample size works well even for small numbers of states.

/pdf/how-much-should-we-trust-differences-in-differences-407p70a9b4.pdf

How Much Should We Trust Differences-In-Differences Estimates?

计量经济分析 = Econometric analysis

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Modern Applied Statistics With S

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Contextual bandit algorithms often estimate reward models to inform decision-making. However, true rewards can contain action-independent redundancies that are not relevant for decision-making. We show it is more data-efficient to estimate any function that explains the reward differences between actions, that is, the treatment effects. Motivated by this observation, building on recent work on oracle-based bandit algorithms, we provide the first reduction of contextual bandits to general-purpose heterogeneous treatment effect estimation, and we design a simple and computationally efficient algorithm based on this reduction. Our theoretical and experimental results demonstrate that heterogeneous treatment effect estimation in contextual bandits offers practical advantages over reward estimation, including more efficient model estimation and greater flexibility to model misspecification.

Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles

This paper evaluates the nature, relative incidence and drivers of software piracy. In contrast to prior studies, we analyze data that allows us to measure piracy for a specific product - Windows 7 - which was associated with a significant level of private sector investment. Using anonymized telemetry data, we are able to characterize the ways in which piracy occurs, the relative incidence of piracy across different economic and institutional environments, and the impact of enforcement efforts on choices to install pirated versus paid software. We find that: (a) the vast majority of "retail piracy" can be attributed to a small number of widely distributed "hacks" that are available through the Internet, (b) the incidence of piracy varies significantly with the microeconomic and institutional environment, and (c) software piracy primarily focuses on the most "advanced" version of Windows (Windows Ultimate). After controlling for a small number of measures of institutional quality and broadband infrastructure, one important candidate driver of piracy - GDP per capita - has no significant impact on the observed piracy rate, while the innovation orientation of an economy is associated with a lower rate of piracy. Finally, we are able to evaluate how piracy changes in response to country-specific anti-piracy enforcement efforts against specific peer-to-peer websites; overall, we find no systematic evidence that such enforcement efforts have had an impact on the incidence of software piracy.

The Nature and Incidence of Software Piracy: Evidence from Windows

Personalized Recommendations in EdTech: Evidence from a Randomized Controlled Trial

Author response: Alpha-1 adrenergic receptor antagonists to prevent hyperinflammation and death from lower respiratory tract infection

We consider a variant of the contextual bandit problem. In standard contextual bandits, when a user arrives we get the user's complete feature vector and then assign a treatment (arm) to that user. In a number of applications (like healthcare), collecting features from users can be costly. To address this issue, we propose algorithms that avoid needless feature collection while maintaining strong regret guarantees.

Susan Athey

Papers

Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles

The Nature and Incidence of Software Piracy: Evidence from Windows

Personalized Recommendations in EdTech: Evidence from a Randomized Controlled Trial

Author response: Alpha-1 adrenergic receptor antagonists to prevent hyperinflammation and death from lower respiratory tract infection

Survey Bandits with Regret Guarantees.