Effects of international sanctions on age-specific mortality: a cross-national panel data analysis

The central role of the propensity score in observational studies for causal effects

Abstract. The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Both large and

The economics of sanctions: From theory into practice | Brookings

Sanctions against Russia after it invaded Ukraine in February 2022 have failed so far to significantly alter Russia’s behavior.

Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal

In many settings, interventions may be more effective for some individuals than others, so that targeting interventions may be beneficial. We analyze the value of targeting in the context of a large-scale field experiment with over 53,000 college students, where the goal was to use "nudges" to encourage students to renew their financial-aid applications before a non-binding deadline. We begin with baseline approaches to targeting. First, we target based on a causal forest that estimates heterogeneous treatment effects and then assigns students to treatment according to those estimated to have the highest treatment effects. Next, we evaluate two alternative targeting policies, one targeting students with low predicted probability of renewing financial aid in the absence of the treatment, the other targeting those with high probability. The predicted baseline outcome is not the ideal criterion for targeting, nor is it a priori clear whether to prioritize low, high, or intermediate predicted probability. Nonetheless, targeting on low baseline outcomes is common in practice, for example because the relationship between individual characteristics and treatment effects is often difficult or impossible to estimate with historical data. We propose hybrid approaches that incorporate the strengths of both predictive approaches (accurate estimation) and causal approaches (correct criterion); we show that targeting intermediate baseline outcomes is most effective in our specific application, while targeting based on low baseline outcomes is detrimental. In one year of the experiment, nudging all students improved early filing by an average of 6.4 percentage points over a baseline average of 37% filing, and we estimate that targeting half of the students using our preferred policy attains around 75% of this benefit.

Why Do Americans Have Shorter Life Expectancy and Worse Health Than Do People in Other High-Income Countries?

Americans lead shorter and less healthy lives than do people in other high-income countries. We review the evidence and explanations for these variations in longevity and health. Our overview suggests that the US health disadvantage applies to multiple mortality and morbidity outcomes. The American health disadvantage begins at birth and extends across the life course, and it is particularly marked for American women and for regions in the US South and Midwest. Proposed explanations include differences in health care, individual behaviors, socioeconomic inequalities, and the built physical environment. Although these factors may contribute to poorer health in America, a focus on proximal causes fails to adequately account for the ubiquity of the US health disadvantage across the life course. We discuss the role of specific public policies and conclude that while multiple causes are implicated, crucial differences in social policy might underlie an important part of the US health disadvantage.

Global aid cuts could lead to 23 million deaths by 2030, study estimates

Lab tests are negative for more than 200 pathogens, but testing continues.

The Global Decline in the Mental Health of the Young

One of the major findings in wellbeing research until recently was that happiness was U-shaped by age and unhappiness hump-shaped by age. There was a midlife crisis — a finding reported in well over 600 published papers using data on 145 countries. It was present in both developing and developed countries. And it was persistent over time. The finding of a peak in ill-being in midlife was consistent with Anne Case and Angus Deaton’s work on mortality and the so-called “deaths of despair.”1 In the US in particular and to a lesser extent in other countries, White prime-aged individuals without a college degree had high death rates from suicide, drug overdoses, and cirrhosis of the liver. Drug overdose deaths in the US, for example, peaked at midlife ages. In 2023, the Centers for Disease Control and Prevention (CDC) reported that overdose death rates per 100,000 by age rose from 13.5 for 15-to-24-year-olds to 45.6 for 25-to-34-year-olds and 60.8 for 35-to-44-year-olds, and then declined to 53.3 for 45-to-54-year-olds, 49.2 for 55-to-64-year-olds, and 14.7 for those over the age of 65.2 The morbidity data matched the mortality data.

Effect of the Tokyo 2020 Summer Olympic Games on COVID-19 incidence in Japan: a synthetic control approach

Background The Tokyo 2020 Summer Olympic Games (23 July–8 August 2021) were held in the middle of Japan’s fifth wave of COVID-19, when the number of cases was on the rise, and coincided with the fourth state of emergency implemented by the host city, Tokyo. Aim This study aimed to assess whether the hosting of the Games was associated with a change in the number of COVID-19 cases in Japan using a synthetic control method. Methods A weighted average of control countries with a variety of predictors was used to estimate the counterfactual trajectory of daily COVID-19 cases per 1 000 000 population in the absence of the Games in Japan. Outcome and predictor data were extracted using official and open sources spanning several countries. The predictors comprise the most recent country-level annual or daily data accessible during the Games, including the stringency of the government’s COVID-19 response, testing capacity and vaccination capacity; human mobility index; electoral democracy index and demographic, socioeconomic, health and weather information. After excluding countries with missing data, 42 countries were ultimately used as control countries. Results The number of observed cases per 1 000 000 population on the last day of the Games was 109.2 (7-day average), which was 115.7% higher than the counterfactual trajectory comprising 51.0 confirmed cases per 1 000 000 population. During the Olympic period (since 23 July), the observed cumulative number of cases was 61.0% higher than the counterfactual trajectory, comprising 143 072 and 89 210 confirmed cases (p=0.023), respectively. The counterfactual trajectory lagged 10 days behind the observed trends. Conclusions Given the increasing likelihood that new emerging infectious diseases will be reported in the future, we believe that the results of this study should serve as a sentinel warning for upcoming mega-events during COVID-19 and future pandemics.

Effect of the Tokyo 2020 Summer Olympic Games on COVID-19 incidence in Japan: a synthetic control approach

Background The Tokyo 2020 Summer Olympic Games (23 July–8 August 2021) were held in the middle of Japan’s fifth wave of COVID-19, when the number of cases was on the rise, and coincided with the fourth state of emergency implemented by the host city, Tokyo. Aim This study aimed to assess whether the hosting of the Games was associated with a change in the number of COVID-19 cases in Japan using a synthetic control method. Methods A weighted average of control countries with a variety of predictors was used to estimate the counterfactual trajectory of daily COVID-19 cases per 1 000 000 population in the absence of the Games in Japan. Outcome and predictor data were extracted using official and open sources spanning several countries. The predictors comprise the most recent country-level annual or daily data accessible during the Games, including the stringency of the government’s COVID-19 response, testing capacity and vaccination capacity; human mobility index; electoral democracy index and demographic, socioeconomic, health and weather information. After excluding countries with missing data, 42 countries were ultimately used as control countries. Results The number of observed cases per 1 000 000 population on the last day of the Games was 109.2 (7-day average), which was 115.7% higher than the counterfactual trajectory comprising 51.0 confirmed cases per 1 000 000 population. During the Olympic period (since 23 July), the observed cumulative number of cases was 61.0% higher than the counterfactual trajectory, comprising 143 072 and 89 210 confirmed cases (p=0.023), respectively. The counterfactual trajectory lagged 10 days behind the observed trends. Conclusions Given the increasing likelihood that new emerging infectious diseases will be reported in the future, we believe that the results of this study should serve as a sentinel warning for upcoming mega-events during COVID-19 and future pandemics.

The C-Word: Scientific Euphemisms Do Not Improve Causal Inference From Observational Data

Causal inference is a core task of science. However, authors and editors often refrain from explicitly acknowledging the causal goal of research projects; they refer to causal effect estimates as associational estimates. This commentary argues that using the term “causal” is necessary to improve the quality of observational research. Specifically, being explicit about the causal objective of a study reduces ambiguity in the scientific question, errors in the data analysis, and excesses in the interpretation of the results.

Cancer’s toll on 9/11 survivors is accelerating 25 years later

The number of victims receiving aid has tripled in recent years, a Post analysis finds, as the illness’s toll becomes more apparent.

Structural adjustment: damages, reparations and pathways to non-recurrence

Beginning in the 1980s and 1990s, the International Monetary Fund (IMF) and the World Bank implemented neoliberal structural adjustment programmes (SAPs) across most countries in Asia, Africa and Latin America. SAPs imposed austerity, privatisation and economic deregulation and have been associated with severe negative impacts on human welfare, including (a) declining real wages and working-class consumption, (b) increased rates of poverty and basic-needs deprivation, (c) increased neonatal and maternal mortality and (d) reduced health system access. Structural adjustment also created conditions for increased financial outflows and drain from the global South through unequal exchange. This paper reviews evidence of these damages and proposes possible options for reparations and distributive justice. We argue that the IMF and the World Bank should be democratised and restructured—or otherwise replaced by alternative institutions—to prevent further harm.

mediation: R Package for Causal Mediation Analysis

In this paper, we describe the R package mediation for conducting causal mediation analysis in applied empirical research. In many scientific disciplines, the goal of researchers is not only estimating causal effects of a treatment but also understanding the process in which the treatment causally affects the outcome. Causal mediation analysis is frequently used to assess potential causal mechanisms. The mediation package implements a comprehensive suite of statistical tools for conducting such an analysis. The package is organized into two distinct approaches. Using the model-based approach, researchers can estimate causal mediation effects and conduct sensitivity analysis under the standard research design. Furthermore, the design-based approach provides several analysis tools that are applicable under different experimental designs. This approach requires weaker assumptions than the model-based approach. We also implement a statistical method for dealing with multiple (causally dependent) mediators, which are often encountered in practice. Finally, the package also offers a methodology for assessing causal mediation in the presence of treatment noncompliance, a common problem in randomized trials.

Recursive partitioning for heterogeneous causal effects

In this paper we propose methods for estimating heterogeneity in causal effects in experimental and observational studies and for conducting hypothesis tests about the magnitude of differences in treatment effects across subsets of the population. We provide a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects. The approach enables the construction of valid confidence intervals for treatment effects, even with many covariates relative to the sample size, and without “sparsity” assumptions. We propose an “honest” approach to estimation, whereby one sample is used to construct the partition and another to estimate treatment effects for each subpopulation. Our approach builds on regression tree methods, modified to optimize for goodness of fit in treatment effects and to account for honest estimation. Our model selection criterion anticipates that bias will be eliminated by honest estimation and also accounts for the effect of making additional splits on the variance of treatment effect estimates within each subpopulation. We address the challenge that the “ground truth” for a causal effect is not observed for any individual unit, so that standard approaches to cross-validation must be modified. Through a simulation study, we show that for our preferred method honest estimation results in nominal coverage for 90% confidence intervals, whereas coverage ranges between 74% and 84% for nonhonest approaches. Honest estimation requires estimating the model with a smaller sample size; the cost in terms of mean squared error of treatment effects for our preferred method ranges between 7–22%.

Environmental epidemiology in a crossfire

Two tendencies have emerged in environmental epidemiology that hamper the translation of research findings into prevention of environmental hazards. One is the increased focus on highlighting weaknesses of epidemiology research that is clearly meant to explain away the research conclusions and weaken their possible implications for interventions to control environmental hazards. Another is the voluminous amount of information sharing that involves a substantial amount of misinformation, as part of the ongoing infodemic. In this light, the appearance of the catalogue of doubt-raising strategies, indeed the worst practices of scientific inference, is good news. Collected under the auspices of the International Network for Epidemiology in Policy, it serves to illustrate the range of possible (and impossible) forms of critique that may be raised on behalf of vested interests or other groups who for some reason disagree with the epidemiological conclusions. We believe that this systematic list will be useful in our field and help to identify critiques of policy options that are hidden and sometimes suppressed in weighing the epidemiological evidence.

Integrative experiments identify how punishment affects welfare in public goods games

Despite decades of research, the conditions under which punishment promotes cooperation remain unclear. Through an integrative experiment varying 14 design parameters of public goods games across 360 experimental conditions (147,618 decisions from 7100 participants), we reveal substantial heterogeneity in punishment effectiveness: Its impact on welfare ranges from 43% improvement to 44% reduction depending on the game parameters. To characterize these patterns, we developed models that outperformed human forecasters in predicting punishment effectiveness in new experiments. Communication emerges as the most important factor, followed by contribution framing (opt out versus opt in), contribution type (variable versus all-or-nothing), game length, and outcome visibility, though these factors often interact. The results reframe the debate from whether punishment works to when it does, demonstrating how integrative experiments enable discovery of generalizable patterns in social phenomena. , Editor’s summary People face conflicts between maximizing personal gain versus supporting collective interests. If we cooperatively recycle or donate to charities, it benefits society, but it also costs us time and resources that could be selfishly preserved for ourselves. We impose penalties to deter those undesirable or selfish behaviors, but under what conditions do punishments or penalties effectively modify behavior to benefit group welfare? Alsobay et al . systematically and simultaneously varied 14 factors together instead of in isolation. Punishment was unequivocally most effective when paired with consistent communication, particularly over time. Another effective factor was “opting out” or withdrawing some, but not all, endowments already in the public fund. These methodological advances revealed when, rather than whether, punishment works. —Ekeoma Uzogara , INTRODUCTION Human societies face many situations where individual and collective interests conflict, often referred to as social dilemmas. Costly peer punishment has been studied for more than 25 years in public goods games (stylized behavioral experiments in which individuals decide how much to contribute to a shared pool that benefits everyone) as a mechanism to promote cooperation. Prior research has identified many contextual factors that moderate punishment’s effectiveness, including game length, communication, group size, punishment cost, and so on. However, the specific conditions under which punishment improves group welfare remain unclear. RATIONALE We argue that this lack of clarity derives from the dominant experimental paradigm, in which any given study manipulates only one or a few theoretically informed factors. Because such studies differ in many ways (different experimental procedures, populations), their results are often difficult to compare or integrate. Consequently, one can list many factors that have some effect, but cannot say how much each matters relative to the others, or how they work together, and as a result, cannot predict when punishment will help or harm welfare in new settings. To address this fundamental knowledge gap, we use an integrative experimental design and systematically vary 14 parameters across 360 conditions (147,618 decisions from 7100 participants) to elucidate when punishment improves versus undermines welfare in public goods games, which factors matter most, and how they interact. RESULTS The effect of punishment on welfare ranged from 43% improvement to 44% reduction depending on the specific combination of game parameters. To characterize this heterogeneity, we trained a model that outperformed all 553 human forecasters (laypeople and experts) in predicting whether punishment would help or harm welfare in new experiments. Communication emerged as roughly three times more important than any other factor, followed by contribution framing (opt in versus opt out), contribution type (variable versus all-or-nothing), game length, and peer outcome visibility (whether participants can see others’ earnings). These factors often interact. For example, longer games enhance punishment’s effectiveness only when communication is available, and contribution framing effects depend on both contribution type and outcome visibility. CONCLUSION Many phenomena in social science are shaped by many factors whose interactions are consequential, yet the dominant experimental paradigm often limits its inquiry to “does a given effect exist?” and examines hypothesized factors in isolation. As a result, research programs can accumulate many partial explanations without a clear picture of how they combine to determine outcomes across settings. Knowing that factors matter individually is fundamentally different from knowing how much each matters and how they interact. The integrative approach implemented here offers one way forward. It varies many factors simultaneously within a shared design space, evaluates models by their predictive accuracy on new experiments, and probes those models to constrain and develop theory. Our hope is that integrative experiment designs, combined with models that integrate prediction and explanation, represent a path toward more cumulative social science. Integrative experiment reveals when punishment helps versus harms. We systematically varied 14 design parameters across 360 experimental conditions. The effect of punishment on cooperation efficiency ranged from −44% to +43% depending on the specific game parameters. Communication emerged as three times more important than any other factor, followed by contribution framing, contribution type, and game length.

Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies

Presents a discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation. The objective was to specify the benefits of randomization in estimating causal effects of treatments. It is concluded that randomization should be employed whenever possible but that the use of carefully controlled nonrandomized data to estimate causal effects is a reasonable and necessary procedure in many cases.