| Title: |
A Primer on Deep Learning for Causal Inference |
| Language: |
English |
| Authors: |
Bernard J. Koch (ORCID 0000-0001-5312-3440); Tim Sainburg; Pablo Geraldo Bastías; Song Jiang; Yizhou Sun; Jacob G. Foster (ORCID 0000-0003-4942-8326) |
| Source: |
Sociological Methods & Research. 2025 54(2):397-447. |
| Availability: |
SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: |
Y |
| Page Count: |
51 |
| Publication Date: |
2025 |
| Document Type: |
Journal Articles; Reports - Descriptive |
| Descriptors: |
Artificial Intelligence; Statistical Inference; Causal Models; Social Science Research |
| DOI: |
10.1177/00491241241234866 |
| ISSN: |
0049-1241; 1552-8294 |
| Abstract: |
This primer systematizes the emerging literature on causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction to building and optimizing custom deep learning models and shows how to adapt them to estimate/predict heterogeneous treatment effects. It also discusses ongoing work to extend causal inference to settings where confounding is nonlinear, time-varying, or encoded in text, networks, and images. To maximize accessibility, we also introduce prerequisite concepts from causal inference and deep learning. The primer 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 and PyTorch. |
| Abstractor: |
As Provided |
| Entry Date: |
2025 |
| Accession Number: |
EJ1473610 |
| Database: |
ERIC |