Causality with Less Modeling

Huntington-Klein, Nick

doi:10.1201/9781003226055-12

Cited by 28 publications

(53 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A quasi-experimental event study design [ 13 ] was used to compare the actual firearm conversation volume post event to forecasted firearm conversation volume under the counterfactual scenario that a shooting had not occurred. Expected volumes were estimated using a seasonal autoregressive integrated moving average time series model with an order of (0, 1, 1) and seasonal order of (1, 1, 1, 7) on daily counts for a pre-event period (March 1 to May 23, 2022) to forecast the firearm conversation volume post event (May 4 to May 30, 2022).…”

Section: Methodsmentioning

confidence: 99%

The Use of Crisis Services Following the Mass School Shooting in Uvalde, Texas: Quasi-Experimental Event Study

Weitzel¹,

Chew²,

Miller³

et al. 2023

JMIR Public Health Surveill

View full text Add to dashboard Cite

Background Mass shootings result in widespread psychological trauma for survivors and members of the affected community. However, less is known about the broader effects of indirect exposure (eg, media) to mass shootings. Crisis lines offer a unique opportunity to examine real-time data on the widespread psychological effects of mass shootings. Objective Crisis Text Line is a not-for-profit company that provides 24/7 confidential SMS text message–based mental health support and crisis intervention service. This study examines changes in the volume and composition of firearm-related conversations at Crisis Text Line before and after the mass school shooting at Robb Elementary School on May 24, 2022, in Uvalde, Texas. Methods A quasi-experimental event study design was used to compare the actual volume of firearm-related conversations received by Crisis Text Line post shooting to forecasted firearm conversation volume under the counterfactual scenario that a shooting had not occurred. Conversations related to firearms were identified among all conversations using keyword searches. Firearm conversation volume was predicted using a seasonal autoregressive integrated moving average model trained on the 3 months of data leading up to the shooting. Additionally, proportions of issue tags (topics coded post conversation by volunteer crisis counselors at Crisis Text Line after the exchange) were compared in the 4 days before (n=251) and after (n=417) the shooting to assess changes in conversation characteristics. The 4-day window was chosen to reflect the number of days conversation volume remained above forecasted levels. Results There was a significant increase in the number of conversations mentioning firearms following the shooting, with the largest spike (compared to forecasted numbers) occurring the day after the shooting (n=159) on May 25, 2022. By May 28, the volume reverted to within the 95% CI of the forecasted volume (n=77). Within firearm conversations, “grief” issue tags showed a significant increase in proportion in the week following the shooting, while “isolation/loneliness,” “relationships,” and “suicide” issue tags showed a significant decrease in proportions the week following the shooting. Conclusions The results suggest that the Uvalde school shooting may have contributed to an increase in demand for crisis services, above what would be expected given historical trends. Additionally, we found that these firearm-related crises conversations immediately post event are more likely to be related to grief and less likely to be related to suicide, loneliness, and relationships. Our findings provide some of the first data showing the real-time repercussions for the broader population exposed to school shooting events. This work adds to a growing evidence base documenting and measuring the rippling effects of mass shootings outside of those directly impacted.

show abstract

Section: Methodsmentioning

confidence: 99%

The Use of Crisis Services Following the Mass School Shooting in Uvalde, Texas: Quasi-Experimental Event Study

Weitzel¹,

Chew²,

Miller³

et al. 2023

JMIR Public Health Surveill

View full text Add to dashboard Cite

show abstract

“…Robustness tests on regression estimates should be conducted on the basis of a reliable causal diagram, similar to a placebo test, where the remaining two variables on the causal diagram are selected, except for treatment and outcome, all paths between them are identified, and all open paths are blocked by the control variable. If the two variables are still correlated with each other, there must be a path that we have not considered, and our causal diagram is flawed, implying that the regression estimates obtained for the causal effect between treatment and outcome may be wrong (Huntington-Klein, 2021).…”

Section: Selection Of Core Control Variables and Robustness Tests Of ...mentioning

confidence: 99%

“…Regression is the most common method used in econometrics to estimate the relationship between two variables (Colin, 2015;Frölich & Sperlich, 2019). Many causal inference methods, such as instrumental variable methods using two-stage least squares estimation, are also based on regression (Huntington-Klein, 2021).…”

Section: Introductionmentioning

confidence: 99%

Causal inference using regression-based statistical control: Confusion in Econometrics

Fan

2023

Journal of Data and Information Science

View full text Add to dashboard Cite

Regression is a widely used econometric tool in research. In observational studies, based on a number of assumptions, regression-based statistical control methods attempt to analyze the causation between treatment and outcome by adding control variables. However, this approach may not produce reliable estimates of causal effects. In addition to the shortcomings of the method, this lack of confidence is mainly related to ambiguous formulations in econometrics, such as the definition of selection bias, selection of core control variables, and method of testing for robustness. Within the framework of the causal models, we clarify the assumption of causal inference using regression-based statistical controls, as described in econometrics, and discuss how to select core control variables to satisfy this assumption and conduct robustness tests for regression estimates.

show abstract

“…Instrumental Variable In the following, the IV regression causal diagram, equations, and assumptions are discussed. The study of Huntington–Klein [ 51 ] has been used as a reference for the mathematical proof.…”

Section: Figure A1mentioning

confidence: 99%

Social Behavior and COVID-19: Analysis of the Social Factors behind Compliance with Interventions across the United States

Maleki

Bahrami

Menéndez

et al. 2022

IJERPH

View full text Add to dashboard Cite

Since its emergence, COVID-19 has caused a great impact in health and social terms. Governments and health authorities have attempted to minimize this impact by enforcing different mandates. Recent studies have addressed the relationship between various socioeconomic variables and compliance level to these interventions. However, little attention has been paid to what constitutes people’s response and whether people behave differently when faced with different interventions. Data collected from different sources show very significant regional differences across the United States. In this paper, we attempt to shed light on the fact that a response may be different depending on the health system capacity and each individuals’ social status. For that, we analyze the correlation between different societal (i.e., education, income levels, population density, etc.) and healthcare capacity-related variables (i.e., hospital occupancy rates, percentage of essential workers, etc.) in relation to people’s level of compliance with three main governmental mandates in the United States: mobility restrictions, mask adoption, and vaccine participation. Our aim was to isolate the most influential variables impacting behavior in response to these policies. We found that there was a significant relationship between individuals’ educational levels and political preferences with respect to compliance with each of these mandates.

show abstract

Causality with Less Modeling

Cited by 28 publications

References 0 publications

The Use of Crisis Services Following the Mass School Shooting in Uvalde, Texas: Quasi-Experimental Event Study

The Use of Crisis Services Following the Mass School Shooting in Uvalde, Texas: Quasi-Experimental Event Study

Causal inference using regression-based statistical control: Confusion in Econometrics

Social Behavior and COVID-19: Analysis of the Social Factors behind Compliance with Interventions across the United States

Contact Info

Product

Resources

About