A Statistical Interrogation of “The Case for Causality, Part 1” by Rausch and Haidt – Matthew B. Jané

Do social media experiments prove a link with mental health: A methodological and meta-analytic review.

The great rewiring: is social media really behind an epidemic of teenage mental illness?

The evidence is equivocal on whether screen time is to blame for rising levels of teen depression and anxiety — and rising hysteria could distract us from tackling the real causes.

The great rewiring: is social media really behind an epidemic of teenage mental illness?

The evidence is equivocal on whether screen time is to blame for rising levels of teen depression and anxiety — and rising hysteria could distract us from tackling the real causes.

Causality

Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence (AI) and systems from The Cognitive Science Society.

Statistical Modeling, Causal Inference, and Social Science

I saw in a recent issue of the Times Literary Supplement that you have been critical of the “chambermaid” study which purported to show that people were losing weight without changing their diet or exercise. I agree that this study did not show what it claimed.

Social media and youth mental health: Simple narratives produce biased interpretations.

Computer-Mediated Communication, Social Media, and Mental Health: A Conceptual and Empirical Meta-Review

Computer-mediated communication (CMC), and specifically social media, may affect the mental health (MH) and well-being of its users, for better or worse. Research on this topic has accumulated rapidly, accompanied by controversial public debate and numerous systematic reviews and meta-analyses. Yet, a higher-level integration of the multiple disparate conceptual and operational approaches to CMC and MH and individual review findings is desperately needed. To this end, we first develop two organizing frameworks that systematize conceptual and operational approaches to CMC and MH. Based on these frameworks, we integrate the literature through a meta-review of 34 reviews and a content analysis of 594 publications. Meta-analytic evidence, overall, suggests a small negative association between social media use and MH. However, effects are complex and depend on the CMC and MH indicators investigated. Based on our conceptual review and the evidence synthesis, we devise an agenda for future research in this interdisciplinary field.

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.

Does Using Social Media Jeopardize Well-Being? The Importance of Separating Within- From Between-Person Effects

Social networking sites (SNS) are frequently criticized as a driving force behind rising depression rates. Yet empirical studies exploring the associations between SNS use and well-being have been predominantly cross-sectional, while the few existing longitudinal studies provided mixed results. We examined prospective associations between SNS use and multiple indicators of well-being in a nationally representative sample of Dutch adults ( N ∼ 10,000), comprising six waves of annual measures of SNS use and well-being. We used an analytic method that estimated prospective effects of SNS use and well-being while also estimating time-invariant between-person associations between these variables. Between individuals, SNS use was associated with lower well-being. However, within individuals, year-to-year changes in SNS use were not prospectively associated with changes in well-being (or vice versa). Overall, our analyses suggest that the conclusions about the causal impact of social media on rising mental health problems in the population might be premature.

Study: Social media probably can’t be fixed

The [structural] mechanism producing these problematic outcomes is really robust and hard to resolve."

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

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.

Experimentally manipulating social media abstinence: results of a four-week diary study

Social media use has a weak, negative association with well-being in cross-sectional and longitudinal research, but this association in experimental studies is mixed. This investigation explores wh...

Social media promotion improves job market outcomes

Social media has transformed how academics disseminate research, but its effect on academic job outcomes remains unclear. Previous research has shown correlations between social media exposure and metrics like citation counts, but these relationships may be confounded by unobserved factors such as researcher quality or access to professional networks. We examine whether social media promotion causally affects job market outcomes in economics through a field experiment on Twitter (now X). We first collect tweets about job market papers from 519 candidates and post them from a dedicated account. We then randomize half of the posts to be quote-tweeted by established economists in the candidates’ fields, and measure the effects on both online visibility and hiring outcomes. We find that posts in the treatment group receive 441% more views and 303% more likes than those in the control group. Candidates whose posts were assigned to be quote-tweeted receive one additional flyout invitation compared to the control group average of 5.4 flyouts. Furthermore, women in the treatment group receive 0.9 more job offers than women in the control group, who receive 3 offers on average. Exploring mechanisms, we find that academic reputation drives these results, with stronger effects for quote-tweets from highly cited scholars and for candidates from top institutions. Our findings suggest social media promotion causally increases research visibility and improves academic job market outcomes.