The course, taught in English, develops the basic skills and knowledge necessary to conduct and understand empirical research in law.
The course focuses on tools for studying causal questions, as opposed to merely describing data or studying formal statistical theory. The starting point for examining causal questions is the controlled experiment, which represents the gold standard in empirical research. In the social sciences, experiments typically take the form of field experiments (randomized controlled trials). The course then turns to identification techniques used outside the setting of controlled experiments.
Upon successful completion of the course, students will be able to:
• conduct empirical research in law using several of the most commonly applied empirical methods;
• analyse social science data, with particular emphasis on data from the legal field;
• critically read and evaluate academic literature in the field of empirical legal research.
Last update: Šicnerová Barbora, Mgr. (16.09.2026)
The course, taught in English, develops the basic skills and knowledge necessary to conduct and understand empirical research in law.
The course focuses on tools for studying causal questions, as opposed to merely describing data or studying formal statistical theory. The starting point for examining causal questions is the controlled experiment, which represents the gold standard in empirical research. In the social sciences, experiments typically take the form of field experiments (randomized controlled trials). The course then turns to identification techniques used outside the setting of controlled experiments.
Upon successful completion of the course, students will be able to:
• conduct empirical research in law using several of the most commonly applied empirical methods;
• analyse social science data, with particular emphasis on data from the legal field;
• critically read and evaluate academic literature in the field of empirical legal research.
Last update: Šicnerová Barbora, Mgr. (16.09.2026)
Teaching methods
Výuka: online včase původních seminářů, a to prostřednictvím MS Teams
Komunikační kanál se studenty: SIS/e-mail/MS Teams
Způsob případné bezkontaktní výuky:
Přednášky budou nahrávány a zpřístupněny zpětně v MS Teams.
Studentům budou zpřístupněny prezentace a další podkladové materiály v SIS a MS Teams.
Písemná zkouška v případě nemožnosti jejího konání kontaktně proběhne bezkontaktně v Moodle.
Last update: Čihák Jakub, Ing., Ph.D. (04.02.2022)
Course assessment methods and requirements for successful completion, grading scheme -
1.The requirements for successful completion of the course are:
·completion of three assignments, the purpose of which is to develop practical data analysis skills;
·active participation in seminars; and
·a final written test.
2.Given the nature of the course, students are not permitted to use legislation or any other sources during the final written test.
Last update: Šicnerová Barbora, Mgr. (16.09.2026)
1.The requirements for successful completion of the course are:
·completion of three assignments, the purpose of which is to develop practical data analysis skills;
·active participation in seminars; and
·a final written test.
2.Given the nature of the course, students are not permitted to use legislation or any other sources during the final written test.
Last update: Šicnerová Barbora, Mgr. (16.09.2026)
Syllabus -
The course covers the following topics:
Causal questions in empirical legal research
Field experiments (randomized controlled trials)
Use of statistical software
Analysis of experimental data: basic statistics, graphs, and tests
Introduction to natural experiments
Regression analysis
Difference-in-differences
Instrumental variables
Regression discontinuity design
Panel data and fixed effects
Last update: Šicnerová Barbora, Mgr. (16.09.2026)
The course covers the following topics:
Causal questions in empirical legal research
Field experiments (randomized controlled trials)
Use of statistical software
Analysis of experimental data: basic statistics, graphs, and tests
Introduction to natural experiments
Regression analysis
Difference-in-differences
Instrumental variables
Regression discontinuity design
Panel data and fixed effects
Last update: Šicnerová Barbora, Mgr. (16.09.2026)
Learning resources -
Required literature:
1.Epstein, L., & Martin, A. D. (2014). An introduction to empirical legal research. Oxford University Press.
2.Angrist Joshua D., and Jorn-Steffen Pischke. 2014. Mastering Metrics: The Path from Cause to Effect. Princeton NJ: Princeton University Press.
Recommended literature:
1.Angrist, Joshua D., and Alan B. Krueger. 1999. Empirical Strategies in Labor Economics. In Handbook of Labor Economics, Volume 3, ed. Orley Asenfelter and David Card. Amsterdam: Elsevier, 1277-1366. Available online at https://www.uh.edu/~adkugler/Angrist&Krueger_HLE.pdf.
2.Engel, Christoph. 2016. Experimental Criminal Law. A Survey of Contributions from Law, Economics and Criminology. In Empirical Legal Research in Action: Reflections on Methods and their Applications, ed. Willem H. van Boom, Pieter Desmet, and Peter Mascini. Chettleham, UK: Edward Elgar. 57-108. Available online at www.ssrn.com/abstract=2769771.
3.Engel, Christoph. 2018. Empirical Methods for the Law. 174: 5--23. Available online at www.ssrn.com/abstract=2966095.
4.Lawless, R. M., Robbennolt, J. K., & Ulen, T. (2010). Empirical methods in law (p. 172). New York: Aspen Publishers.
5.Murray, Michael P. 1994. A drunk and her dog: an illustration of cointegration and error correction. 48: 37-39.
Last update: Šicnerová Barbora, Mgr. (16.09.2026)
Required literature:
1.Epstein, L., & Martin, A. D. (2014). An introduction to empirical legal research. Oxford University Press.
2.Angrist Joshua D., and Jorn-Steffen Pischke. 2014. Mastering Metrics: The Path from Cause to Effect. Princeton NJ: Princeton University Press.
Recommended literature:
1.Angrist, Joshua D., and Alan B. Krueger. 1999. Empirical Strategies in Labor Economics. In Handbook of Labor Economics, Volume 3, ed. Orley Asenfelter and David Card. Amsterdam: Elsevier, 1277-1366. Available online at https://www.uh.edu/~adkugler/Angrist&Krueger_HLE.pdf.
2.Engel, Christoph. 2016. Experimental Criminal Law. A Survey of Contributions from Law, Economics and Criminology. In Empirical Legal Research in Action: Reflections on Methods and their Applications, ed. Willem H. van Boom, Pieter Desmet, and Peter Mascini. Chettleham, UK: Edward Elgar. 57-108. Available online at www.ssrn.com/abstract=2769771.
3.Engel, Christoph. 2018. Empirical Methods for the Law. 174: 5--23. Available online at www.ssrn.com/abstract=2966095.
4.Lawless, R. M., Robbennolt, J. K., & Ulen, T. (2010). Empirical methods in law (p. 172). New York: Aspen Publishers.
5.Murray, Michael P. 1994. A drunk and her dog: an illustration of cointegration and error correction. 48: 37-39.
Last update: Šicnerová Barbora, Mgr. (16.09.2026)
Learning outcomes -
Knowledge
By the end of this course, students will be able to explain and identify:
Statistical significance vs. practical significance: why a statistically significant result may nevertheless be irrelevant for legal policy-making;
Internal vs. external validity: the trade-off between establishing causality within a particular study and generalizing the results to a broader population;
Type I and Type II errors: the statistical equivalents of “false convictions” and “false acquittals”;
Measurement error: how poorly defined legal variables (e.g., “recidivism”) can bias empirical findings;
The fundamental problem of causal inference: why we can never observe the same unit simultaneously under both treatment and no-treatment conditions;
Selection bias: how self-selection into legal interventions (e.g., the decision to settle a case) creates systematic differences between groups;
The potential outcomes framework: the logic of counterfactual outcomes in legal contexts;
Directed acyclic graphs (DAGs): the theoretical role of nodes and arrows in visualizing causal assumptions and identifying “backdoor” paths;
Randomized controlled trials (RCTs): why random assignment is considered the “gold standard” for internal validity when evaluating legal policies;
Omitted variable bias: how failing to account for unobserved factors (e.g., a defendant’s personal background) can produce “spurious” correlations in data, such as sentencing data;
Difference-in-differences (DiD): the logic of the “parallel trends” assumption when comparing jurisdictions that changed a law with those that did not;
Regression discontinuity (RD): how arbitrary legal thresholds (e.g., the age of majority or cut-offs in sentencing guidelines) can create “natural” experiments;
Instrumental variables (IV): the concept of an “exogenous shock” that affects treatment but has no direct effect on the outcome.
Skills:
By the end of this course, students will be able to:
·Analyse expert testimony: identify shortcomings in the statistical methodology presented by expert witnesses in legal proceedings;
·Map causal claims: draw a DAG for a specific legal hypothesis and identify potential colliders and mediators;
·Interpret regression tables: read and explain coefficients, standard errors, and significance stars in a standard empirical legal studies article;
·Critically assess the confusion of correlation with causation: systematically analyze media reports or policy briefs that mistake association for causation;
·Formulate testable hypotheses: translate a vague legal theory into a specific empirical question that can be falsified;
·Assess “parallel trends”: evaluate, both visually and statistically, whether the assumptions of a Difference-in-Differences (DiD) design are satisfied;
·Identify natural experiments: recognize opportunities in legal settings that allow for causal estimation;
·Evaluate sample representativeness: determine whether a study’s data (e.g., from a single district court) can justify a nationwide policy change;
·Communicate uncertainty: explain confidence intervals to a non-technical audience (e.g., a judge or client);
·Conduct a literature review: synthesize conflicting empirical findings on a legal issue (e.g., the deterrent effect of the death penalty);
·Identify confounding variables: identify at least three “hidden” factors that could explain the correlation between two legal phenomena
Last update: Šicnerová Barbora, Mgr. (16.09.2026)
Knowledge
By the end of this course, students will be able to explain and identify:
Statistical significance vs. practical significance: why a statistically significant result may nevertheless be irrelevant for legal policy-making;
Internal vs. external validity: the trade-off between establishing causality within a particular study and generalizing the results to a broader population;
Type I and Type II errors: the statistical equivalents of “false convictions” and “false acquittals”;
Measurement error: how poorly defined legal variables (e.g., “recidivism”) can bias empirical findings;
The fundamental problem of causal inference: why we can never observe the same unit simultaneously under both treatment and no-treatment conditions;
Selection bias: how self-selection into legal interventions (e.g., the decision to settle a case) creates systematic differences between groups;
The potential outcomes framework: the logic of counterfactual outcomes in legal contexts;
Directed acyclic graphs (DAGs): the theoretical role of nodes and arrows in visualizing causal assumptions and identifying “backdoor” paths;
Randomized controlled trials (RCTs): why random assignment is considered the “gold standard” for internal validity when evaluating legal policies;
Omitted variable bias: how failing to account for unobserved factors (e.g., a defendant’s personal background) can produce “spurious” correlations in data, such as sentencing data;
Difference-in-differences (DiD): the logic of the “parallel trends” assumption when comparing jurisdictions that changed a law with those that did not;
Regression discontinuity (RD): how arbitrary legal thresholds (e.g., the age of majority or cut-offs in sentencing guidelines) can create “natural” experiments;
Instrumental variables (IV): the concept of an “exogenous shock” that affects treatment but has no direct effect on the outcome.
Skills:
By the end of this course, students will be able to:
·Analyse expert testimony: identify shortcomings in the statistical methodology presented by expert witnesses in legal proceedings;
·Map causal claims: draw a DAG for a specific legal hypothesis and identify potential colliders and mediators;
·Interpret regression tables: read and explain coefficients, standard errors, and significance stars in a standard empirical legal studies article;
·Critically assess the confusion of correlation with causation: systematically analyze media reports or policy briefs that mistake association for causation;
·Formulate testable hypotheses: translate a vague legal theory into a specific empirical question that can be falsified;
·Assess “parallel trends”: evaluate, both visually and statistically, whether the assumptions of a Difference-in-Differences (DiD) design are satisfied;
·Identify natural experiments: recognize opportunities in legal settings that allow for causal estimation;
·Evaluate sample representativeness: determine whether a study’s data (e.g., from a single district court) can justify a nationwide policy change;
·Communicate uncertainty: explain confidence intervals to a non-technical audience (e.g., a judge or client);
·Conduct a literature review: synthesize conflicting empirical findings on a legal issue (e.g., the deterrent effect of the death penalty);
·Identify confounding variables: identify at least three “hidden” factors that could explain the correlation between two legal phenomena
Last update: Šicnerová Barbora, Mgr. (16.09.2026)
Registration requirements -
For enrolment in the course, it is recommended that students have completed Statistics for Lawyers and Introduction to Social Science Methodology for Lawyers, or equivalent courses. The course requires prior familiarity with basic statistics and/or econometrics. Before enrolling in the course, students must have completed an introductory statistics course (or equivalent). Prior research experience or completion of a research methods course is an advantage.
Last update: Šicnerová Barbora, Mgr. (16.09.2026)
For enrolment in the course, it is recommended that students have completed Statistics for Lawyers and Introduction to Social Science Methodology for Lawyers, or equivalent courses. The course requires prior familiarity with basic statistics and/or econometrics. Before enrolling in the course, students must have completed an introductory statistics course (or equivalent). Prior research experience or completion of a research methods course is an advantage.
