Real Effect or Bias? Good Practices for Evaluating the Robustness of Evidence From Comparative Observational Studies Through Quantitative Sensitivity Analysis for Unmeasured Confounding.

The assumption of "no unmeasured confounders" is a critical but unverifiable assumption required for causal inference yet quantitative sensitivity analyses to assess robustness of real-world evidence remains under-utilized. The lack of use is likely in part due to complexity of implementation and often specific and restrictive data requirements for application of each method. With the advent of methods that are broadly applicable in that they do not require identification of a specific unmeasured confounder-along with publicly available code for implementation-roadblocks toward broader use of sensitivity analyses are decreasing.

Better individual-level risk models can improve the targeting and life-saving potential of early-mortality interventions.

Infant mortality remains high and uneven in much of sub-Saharan Africa. Even low-cost, highly effective therapies can only save lives in proportion to how successfully they can be targeted to those children who, absent the treatment, would have died. This places great value on maximizing the accuracy of any targeting or means-testing algorithm.

Political leadership has limited impact on fossil fuel taxes and subsidies.

For countries to rapidly decarbonize, they need strong leadership, according to both academic studies and popular accounts. But leadership is difficult to measure, and its importance is unclear. We use original data to investigate the role of presidents, prime ministers, and monarchs in 155 countries from 1990 to 2015 in changing their countries' gasoline taxes and subsidies.