ECON 8825 Econometric Methods for Causal Inference

This course covers a modern approach to the estimation of causal treatment effects. Topics covered include the estimation of treatment effects under unconfoundedness, instrumental variables, regression discontinuity, and a range of panel data methods (diff-in-diff, synthetic controls, fixed effects). We also discuss nonparametric estimation techniques, including kernel regression and machine learning methods, focussing on how these can be incorporated into treatment effect estimators.

Course overview

Department
Economics
School
MCAS
Credits
3
Level
Graduate
Offered
Annually

Course evaluations

Response-weighted averages from Boston College’s official course evaluations (Avalanche/Blue).

Course rating
4.95 / 5 · Excellent
21 responses
Instructor rating
4.95 / 5 · Excellent
21 responses
Terms evaluated
3
Most recent: Fall 2025

Scales: overall ratings run from 1 (poor) to 5 (excellent); agreement questions from 1 (strongly disagree) to 5 (strongly agree); weekly effort from 1 (up to 1 hour) to 5 (10 or more hours).

Evaluations by term and instructor
TermInstructorCourse overallInstructor overallWell organizedClear learning objectivesIntellectually challengingHelpful assignmentsApplicable beyond courseAttendance necessaryWeekly effortResponses
Fall 2025David Hughes5.05.05.05.05.05.05.05.04–6 hrs/week9 of 9
Fall 2024David Hughes5.05.05.05.05.05.05.04.910+ hrs/week9 of 11
Fall 2023David Hughes—————————3 of 5

Data freshness

Course and evaluation data last updated 2026-10-09. Source details and limitations are documented in Data Sources and Methodology.

Instructors

  • David Hughes
    Fall 2026, Fall 2025, Fall 2024, Fall 2023 · Official rating 4.95/5

Sections

  • Section 01
    Fall 2026 · David Hughes · Maloney Hall 313 F 10:00AM-12:30PM · Offered