Individuals do not respond uniformly to treatments, such as events or interventions. Sociologists routinely partition samples into subgroups to explore how the effects of treatments vary by selected covariates, such as race and gender, on the basis of theoretical priors. Data-driven discoveries are also routine, yet the analyses by which sociologists typically go about them are often problematic and seldom move us beyond our biases to explore new meaningful subgroups. Emerging machine learning methods based on decision trees allow researchers to explore sources of variation that they may not have previously considered or envisaged. In this article, the authors use tree-based machine learning, that is, causal trees, to recursively partition the sample to uncover sources of effect heterogeneity. Assessing a central topic in social inequality, college effects on wages, the authors compare what is learned from covariate and propensity score–based partitioning approaches with recursive partitioning based on causal trees. Decision trees, although superseded by forests for estimation, can be used to uncover subpopulations responsive to treatments. Using observational data, the authors expand on the existing causal tree literature by applying leaf-specific effect estimation strategies to adjust for observed confounding, including inverse propensity weighting, nearest neighbor matching, and doubly robust causal forests. We also assess localized balance metrics and sensitivity analyses to address the possibility of differential imbalance and unobserved confounding. The authors encourage researchers to follow similar data exploration practices in their work on variation in sociological effects and offer a straightforward framework by which to do so.
This review systematizes the emerging literature for causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction on how deep learning can be used to estimate/predict heterogeneous treatment effects and extend causal inference to settings where confounding is non-linear, time varying, or encoded in text, networks, and images. To maximize accessibility, we also introduce prerequisite concepts from causal inference and deep learning. The survey differs from other treatments of deep learning and causal inference in its sharp focus on observational causal estimation, its extended exposition of key algorithms, and its detailed tutorials for implementing, training, and selecting among deep estimators in Tensorflow 2 available at github.com/kochbj/Deep-Learning-for-Causal-Inference.
Individuals do not respond uniformly to treatments, events, or interventions. Sociologists routinely partition samples into subgroups to explore how the effects of treatments vary by covariates like race, gender, and socioeconomic status. In so doing, analysts determine the key subpopulations based on theoretical priors. Datadriven discoveries are also routine, yet the analyses by which sociologists typically go about them are problematic and seldom move us beyond our expectations, and biases, to explore new meaningful subgroups. Emerging machine learning methods allow researchers to explore sources of variation that they may not have previously considered, or envisaged. In this paper, we use causal trees to recursively partition the sample and uncover sources of treatment effect heterogeneity. We use honest of science, cultural evolution, and computational methods. He is currently focused on the application of deep learning to network and causal inference problems to help identify how we can make science more equitable, efficient, and productive.Pablo Geraldo is a Ph.D. student in Sociology at UCLA and student affiliate of the California Center for Population Research (CCPR). His research examines inequality in education and the labor market, using a mixture of causal inference, network analysis, and machine learning approaches.
This article presents a pilot study exploring the applicability of a linguistically adapted, solution-focused brief therapy (SFBT) program, implemented by social workers in Chilean primary care. Method: We completed a single-case design with eight replications. To analyze the results of the program on participants' alcohol use and other related variables, we conducted visual and percentage of nonoverlapping data analyses. Results: Social workers successfully implemented 10 of the 13 SFBT techniques. Although results need to be interpreted with caution, positive trends were observed. Participants increased their "percentage of days abstinent," diminished "consequences of alcohol use," decreased their "depression index," and increased their "selfreported well-being." Discussion: Results are consistent with previous studies on SFBT and alcohol use. Exception and coping questions may serve to increase abstinent days. SFBT focus on issues other than alcohol that are important to clients could help to reduce harm on individuals who use alcohol.
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