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How much do phd causal inference jobs pay per year?

As of Aug 18, 2026, the average yearly pay for phd causal inference in Indiana is $116,974.00, according to ZipRecruiter salary data. Most workers in this role earn between $99,900.00 and $131,300.00 per year, depending on experience, location, and employer.

What is a PhD in causal inference?

A PhD in Causal Inference is an advanced research degree focused on understanding and identifying cause-and-effect relationships using statistical and computational methods. Students in this field learn to design studies, analyze data, and develop new methodologies to answer complex causal questions in areas such as social sciences, medicine, economics, and artificial intelligence. Graduates often work in academia, research institutions, or industries where evidence-based decision-making is essential.

What are the key skills and qualifications needed to thrive as a PhD causal inference researcher?

To thrive as a PhD Causal Inference researcher, you need advanced knowledge of statistics, econometrics, and causal modeling, typically supported by a doctoral degree in a quantitative field. Familiarity with statistical programming languages (such as R or Python), specialized software (like STATA or SAS), and experience with experimental or quasi-experimental methods are essential. Strong analytical thinking, attention to detail, and the ability to communicate complex findings clearly make a candidate stand out. These skills ensure rigorous, credible research that can inform policy, product development, or scientific understanding by accurately identifying causal relationships.

What collaborative opportunities can a PhD specializing in causal inference expect within a multidisciplinary research team?

PhD professionals in Causal Inference frequently collaborate with experts from fields such as epidemiology, economics, computer science, and public health. They often work closely with data scientists, subject matter experts, and statisticians to design studies, interpret complex datasets, and develop robust analytical models. This multidisciplinary environment fosters continuous learning and often leads to co-authorship on research publications, participation in grant writing, and involvement in high-impact policy or product decisions. Effective communication and teamwork skills are essential to translate technical findings for diverse audiences and drive actionable insights.

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Phd Causal Inference jobs near you

Assistant Research Professor, Lucy Family Institute for Data & Society

University of Notre Dame

Notre Dame, IN • On-site

Full-time

Posted 19 days ago

University Of Notre Dame rating

7.4

Company rating: 7.4 out of 10

Based on 45 frontline employees who took The Breakroom Quiz

337th of 618 rated colleges and universities

Job description

Description
The Lucy Family Institute for Data & Society at the University of Notre Dame seeks an assistant-level research professor (non tenure-track). We prefer candidates with strong expertise in advanced causal inference and computational social science methods including econometric modeling of observational data, survey design, randomized control trials and digital experiments, machine learning (ML) to construct ML-based regressors, and causal ML. The faculty member will be part of a new digital engagement for learning analytics research group within Lucy. As such, research experience with digital engagement measurement, online gaming analytics, and AI-enabled policy impact analysis will be beneficial. Given the policy-oriented learning translation analytics aspect of some of the research, ideal candidates will also have a robust track record of teaching excellence demonstrated through multiple years and sections of lead-instructor course delivery (with high teaching evaluations). The research faculty will be working closely with Professors Ahmed Abbasi (Director of the Institute), Rick Johnson (Associate Director of the Institute), and Sugana Chawla (Data Science Education Program Director).
The ideal candidate would have evidence of excellence in research and scholarship. The ideal candidate would have also demonstrated an interest in interdisciplinary work, as evidenced through projects or research publications.
Expectations
  • Help establish a research program for robust measurement and causal inference in digital settings including but not limited to online video games.
  • Publish in top venues, and/or pipeline evidence such as revise-and-resubmits at top academic journals (e.g., UTD-24, Economics, Science/Nature/PNAS, etc.);
  • Pursue interdisciplinary research by building collaborations;
  • Mentor or co-mentor graduate and undergraduate students;
  • Lead and collaborate on research grants;
  • Teach / co-teach courses on related topics;

Qualifications
  • Requires a PhD in with methodological expertise in causal inference via econometric modeling, machine learning, analysis of digital trace and survey data, and experiment design.
  • Research experience (projects, papers) related to digital engagement, online gaming telemetry data, and analysis of policy impact and implications, with a track record of experience beyond doctoral studies in the domain.
  • Strong demonstrated teaching skills.

Application Instructions
Please submit a CV, a research statement, teaching statement, and three confidential letters of recommendation via Interfolio.

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