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Causal Inference Jobs (NOW HIRING)

Upenn

$85K/yr

The Center for Causal Inference (CCI) in the Department of Biostatistics, Epidemiology & Informatics (DBEI) This position is open to applications from US citizens and foreign nationals. The Center ...

Triwill Group

$200 - $250/hr

Empower our Data Science team (50+ members) to use more rigorous causal inference methods. * In addition to consulting within the wider Data Science team (50+ members), lead and conduct causal ...

Jackalope Digital LLC

$220K - $245K/yr

Empower our Data Science team (50+ members) to use more rigorous causal inference methods. * In addition to consulting within the wider Data Science team (50+ members), lead and conduct causal ...

The Voleon Group

Member of Research Staff, Causal Inference, Voleon Securities Location Employment Type Full time Location Type Hybrid Department Securities Compensation The listed base salary range for this position ...

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How much do causal inference jobs pay per year?

As of Sep 9, 2026, the average yearly pay for causal inference in the United States is $99,231.00, according to ZipRecruiter salary data. Most workers in this role earn between $86,000.00 and $108,500.00 per year, depending on experience, location, and employer.

What is a causal inference?

A Causal Inference job involves using statistical and computational methods to determine cause-and-effect relationships from data. Professionals in this field work with observational and experimental data to identify causal impacts, often in domains like economics, healthcare, social sciences, and technology. They apply techniques such as propensity score matching, instrumental variables, and difference-in-differences to ensure rigorous analysis. These roles are commonly found in academia, policy research, and data science teams within tech and finance companies. Strong skills in statistics, programming (e.g., Python, R), and experimental design are typically required.

What skills and qualifications are needed for a causal inference position?

Success in a Causal Inference role requires strong statistical knowledge, expertise in experimental and quasi-experimental methodologies, and advanced proficiency in programming languages like R or Python, typically acquired with an advanced degree in statistics, economics, data science, or a related field. Familiarity with specialized statistical software (such as Stata, SAS, or causal inference packages in R/Python), as well as experience with large datasets and machine learning tools, is highly valued. Excellent problem-solving abilities, clear communication, and collaboration skills are essential soft skills for effectively conveying complex findings to diverse teams. These competencies are critical to producing reliable insights that guide evidence-based decision-making in business, healthcare, or policy settings.

What are common challenges faced in a causal inference position?

Professionals in Causal Inference often encounter challenges such as dealing with confounding factors, addressing selection bias, and ensuring the validity of assumptions behind statistical models. They must carefully design experiments or leverage observational data while staying vigilant about potential data quality issues and model limitations. Collaboration with subject matter experts, data engineers, and business stakeholders is common to ensure accurate contextualization of results. Overcoming these challenges requires a mix of technical acumen and strong communication skills to translate complex analyses into actionable recommendations.

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Infographic showing various Causal Inference job openings in the United States as of September 2026, with employment types broken down into 6% Internship, 79% Full Time, 14% Part Time, and 1% Contract. Highlights an 65% Physical, 2% Hybrid, and 33% Remote job distribution, with an average salary of $99,231 per year, or $47.7 per hour.

Causal Inference Postdoctoral Researcher - Center for Causal Inference, University of Pennsylvania

On-site

Upenn
See more jobs at Upennexternal

$85K/yr

Other

Posted 8 days ago

Key responsibilities

  • develop new theory and methods in causal inference

  • develop software related to causal inference

  • participate in weekly working group meetings and the annual Causal Inference Summer Institute

Job description

University of Pennsylvania: Postdoctoral Positions: Perelman School of Medicine PostdoctoralLocation

University of Pennsylvania - School of Medicine

Open DateDescription

Department: The Center for Causal Inference (CCI) in the Department of Biostatistics, Epidemiology & Informatics (DBEI)

This position is open to applications from US citizens and foreign nationals.

The Center for Causal Inference (CCI) is a research center in the Department of Biostatistics, Epidemiology and Informatics in the Perelman School of Medicine of the University of Pennsylvania (https://www.dbeicoe.med.upenn.edu/cci/about-us ) with close collaboration with the Wharton Statistics Department. The CCI is a multidisciplinary center that includes faculty, postdocs, and graduate students from Biostatistics, Statistics, Epidemiology, Criminology, Computer Science, Philosophy, and other disciplines. The mission of the CCI is to be the leading center for research and training in the development and application of causal inference theory and methods, with far-ranging applications in Health and Social Sciences.

The position is for a postdoctoral researcher in causal inference with considerable flexibility in terms of research focus, reflecting the interdisciplinary nature of the field. In particular, the postdoctoral researcher will have an opportunity to collaborate on a broad range of methodological projects under the mentorship of one or more faculty affiliated with CCI.

Sample content areas include:

  • Developing methods to study the effect of antidiabetic treatments on the recurrence of hospitalizations for T2DM-related complications.
  • Developing methods for constructing just-in-time adaptive interventions using mobile health (mHealth) data.
  • Developing novel causal inference methods to reconcile possibly conflicting results between randomized trials or randomized trials and observational studies, particularly in the area of pregnancy research.

In addition, the researcher will receive mentorship for professional development from the CCI co-directors Professors Nandita Mitra and Eric Tchetgen Tchetgen, as well as dedicated mentorship based on project type, with the expectation that their time as a researcher will advance their career development.

Duties and Responsibilities: develop new theory and methods in causal inference; develop software; and potentially collaborate on applied projects in Health and Social Sciences. In addition, the fellow is expected to be an active member of the CCI, by, for example, participating in the weekly working group meeting and the annual Causal Inference Summer Institute.

Qualifications

Candidates should have a doctoral degree in biostatistics, statistics, or a related field. Strong computational skills and expertise in causal inference are desired but not required. Start date and term are negotiable.

The University of Pennsylvania is an equal opportunity employer. Candidates are considered for employment without regard to race, color, sex, sexual orientation, religion, creed, national origin (including shared ancestry or ethnic characteristics), citizenship status, age, disability, veteran status or any class protected under applicable federal, state, or local law.

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