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  1. Free, publicly-accessible full text available June 25, 2024
  2. This paper provides a critical review of the Bayesian perspective of causal inference based on the potential outcomes framework. We review the causal estimands, assignment mechanism, the general structure of Bayesian inference of causal effects and sensitivity analysis. We highlight issues that are unique to Bayesian causal inference, including the role of the propensity score, the definition of identifiability, the choice of priors in both low- and high-dimensional regimes. We point out the central role of covariate overlap and more generally the design stage in Bayesian causal inference. We extend the discussion to two complex assignment mechanisms: instrumental variable and time-varying treatments. We identify the strengths and weaknesses of the Bayesian approach to causal inference. Throughout, we illustrate the key concepts via examples. This article is part of the theme issue ‘Bayesian inference: challenges, perspectives, and prospects’. 
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    Free, publicly-accessible full text available May 15, 2024
  3. Abstract Many causal processes have spatial and temporal dimensions. Yet the classic causal inference framework is not directly applicable when the treatment and outcome variables are generated by spatio-temporal point processes. We extend the potential outcomes framework to these settings by formulating the treatment point process as a stochastic intervention. Our causal estimands include the expected number of outcome events in a specified area under a particular stochastic treatment assignment strategy. Our methodology allows for arbitrary patterns of spatial spillover and temporal carryover effects. Using martingale theory, we show that the proposed estimator is consistent and asymptotically normal as the number of time periods increases. We propose a sensitivity analysis for the possible existence of unmeasured confounders, and extend it to the Hájek estimator. Simulation studies are conducted to examine the estimators' finite sample performance. Finally, we illustrate the proposed methods by estimating the effects of American airstrikes on insurgent violence in Iraq from February 2007 to July 2008. Our analysis suggests that increasing the average number of daily airstrikes for up to 1 month may result in more insurgent attacks. We also find some evidence that airstrikes can displace attacks from Baghdad to new locations up to 400 km away. 
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  4. “Concentrate-and-degrade” is an effective strategy to promote mass transfer and degradation of pollutants in photocatalytic systems, yet suitable and cost-effective photocatalysts are required to practice the new concept. In this study, we doped a post-transition metal of Indium (In) on a novel composite adsorptive photocatalyst, activated carbon-supported titanate nanotubes (TNTs@AC), to effectively degrade perfluorooctanoic acid (PFOA). In/TNTs@AC exhibited both excellent PFOA adsorption (>99% in 30 min) and photodegradation (>99% in 4 h) under optimal conditions (25 °C, pH 7, 1 atm, 1 g/L catalyst, 0.1 mg/L PFOA, 254 nm). The heterojunction structure of the composite facilitated a cooperative adsorption mode of PFOA, i.e., binding of the carboxylic head group of PFOA to the metal oxide and attachment of the hydrophobic tail to AC. The resulting side-on adsorption mode facilitates the electron (e‒) transfer from the carboxylic head to the photogenerated hole (h+), which was the major oxidant verified by scavenger tests. Furthermore, the presence of In enables direct electron transfer and facilitates the subsequent stepwise defluorination. Finally, In/TNTs@AC was amenable to repeated uses in four consecutive adsorption-photodegradation runs. The findings showed that adsorptive photocatalysts can be prepared by hybridization of carbon and photoactive semiconductors and the enabled “concentrate-and-degrade” strategy is promising for the removal and degradation of trace levels of PFOA from polluted waters. 
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  5. Many causal processes have spatial and temporal dimensions. Yet the classic causal inference framework is not directly applicable when the treatment and outcome variables are generated by spatio-temporal point processes. We extend the potential outcomes framework to these settings by formulating the treatment point process as a stochastic intervention. Our causal estimands include the expected number of outcome events in a specified area under a particular stochastic treatment assignment strategy. Our methodology allows for arbitrary patterns of spatial spillover and temporal carryover effects. Using martingale theory, we show that the proposed estimator is consistent and asymptotically normal as the number of time periods increases. We propose a sensitivity analysis for the possible existence of unmeasured confounders, and extend it to the Hájek estimator. Simulation studies are conducted to examine the estimators' finite sample performance. Finally, we illustrate the proposed methods by estimating the effects of American airstrikes on insurgent violence in Iraq from February 2007 to July 2008. Our analysis suggests that increasing the average number of daily airstrikes for up to 1 month may result in more insurgent attacks. We also find some evidence that airstrikes can displace attacks from Baghdad to new locations up to 400 km away. 
    more » « less
  6. null (Ed.)