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How much do phd causal inference jobs pay per year?

As of Aug 20, 2026, the average yearly pay for phd causal inference in Connecticut is $116,940.00, 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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Postdoctoral Associate Position in Pharmacoepidemiology, Perinatal Epidemiology, and Causal Inferenc

Yale University

New Haven, CT • On-site

Full-time

Re-posted 8 days ago

Yale University rating

8.6

Company rating: 8.6 out of 10

Based on 64 frontline employees who took The Breakroom Quiz

67th of 620 rated colleges and universities

Job description

Description
Posting date: November 15th, 2025
Title of the Position: Postdoctoral Associate
School: Yale School of Public Health
Department: Department of Environmental Health Sciences, Yale Center for Perinatal, Pediatric and Environmental Epidemiology
Anticipated Appointment Date: February 15th, 2026
Fixed Term: One year, Full-time (renewable)
The Liew lab at the Yale School of Public Health (YSPH) and the Yale Center for Perinatal, Pediatric, and Environmental Epidemiology (CPPEE) is inviting qualified individuals to apply for a Postdoctoral Associate position in the multidisciplinary fields of pharmacoepidemiology, perinatal epidemiology, and causal inference, starting on February 15th, 2026, or soon thereafter. The position is a one-year, full-time role, renewable contingent upon performance and funding. The position is supported by a Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) project. The Postdoctoral Associate will collaborate with a renowned national and international team to develop cutting-edge epidemiologic methods and investigate the heterogeneity of medication effects during pregnancy and child development. The Postdoctoral Associate will have opportunities to work with the Danish National Birth Cohort (DNBC) in Denmark, as well as with US MarketScan data and Yale electronic healthcare data. The research partners include the Yale Pharmacoepidemiology Working Group (Yale PEW) and the Practical Causal Inference (PCI) lab at UCLA.
Qualifications
Candidates should possess a PhD, preferably in epidemiology, biostatistics, and/or health data sciences. Candidates with research experience analyzing longitudinal cohort or health registry data using causal inference methods, as well as those with research interests relevant to perinatal or pharmacoepidemiology, are preferred.
The position will require extensive experience with programming languages and statistical software packages, such as R or SAS. The Postdoctoral Associate position will require excellent interpersonal skills to facilitate effective communications and collaborations across local, national, and international research teams. The Postdoctoral Associate position requires motivation to work in an inclusive team-oriented environment, strong scientific integrity, and a high level of competency in English, both in written and oral formats.
Salary will be based on the Yale Postdoctoral Compensation policy, and support for travel to conferences may also be available.
Application Instructions
Yale University will use Interfolio to search for this position. Applicants receive a free Dossier account and can send all application materials at no cost.
Please apply online.
To apply, please submit a CV, a cover letter, and contact information for three references. In the cover letter to the application package, please summarize your relevant research experience, indicate your specific research interests, and the date you will be available to start.
For questions, please contact the Yale Center for Perinatal, Pediatric and Environmental Epidemiology, Email: cppee@yale.edu

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