Teaching – Dean Eckles

Research opportunities

I am looking for interested MIT students, including undergraduate and graduate students, who want to contribute to research on social networks, applied graph theory, habits, and statistics.

Teaching at MIT

I am on sabbatical for the 2022–2023 academic year. I expect to be teaching again in 2023–2024.

Marketing and product analytics

Spring 2024. 15.819.
Information on the previous version (titled “Marketing analytics”) is here.

Design and analysis of experiments

Fall 2023. 15.838.
Doctoral seminar with a combination of (a) methodological readings and lectures on experimental design and randomization inference and policy learning, especially for field experiments, and (b) critical readings of important work using randomized experiments. Syllabus from 2019 version.

Topics in research design

Spring 2022. 15.838.
Doctoral seminar on designing empirical research, with a focus on causal inference. The course pairs methodological topics with relevant empirical studies. Topics include regression discontinuity designs, partial identification, and sensitivity analysis. Syllabus.

Free online courses

Exploratory data analysis with R

Massive open online course (MOOC) on Udacity. With Moira Burke, Chris Saden, and Solomon Messing. Enroll for free here.

Previous teaching at Stanford University

Communication 114S. Persuasion, Contagion, and Compliance-Gaining in Online Media

Summer 2011. Syllabus.

Teaching as teaching assistant:

CS 378. Phenomenological foundations of language, cognition, and computation. Terry Winograd. Fall 2009, Fall 2011.
CS 377P. Persuading people online and via mobile phones. B.J. Fogg. Fall 2006.

Algorithmic transparency and assessing effects of algorithmic ranking.
Testimony before the US Senate Subcommittee on Communications, Media, and Broadband.
This addresses the questions: How does algorithmic ranking in social media work? How can we assess algorithmic impact & amplification? What can policy-makers do here?

Providing normative information increases intentions to accept a COVID-19 vaccine. Nature Communications.
When does sharing information about others’ behaviors cause more of that same behavior? This is both a central question in the social sciences and also important for effecting preventative health behaviors, including vaccination. We use a randomized experiment embedded in the international survey on COVID-19 we conducted with Meta and JHU to address this, providing evidence that these descriptive norms increase intentions to get vaccinated.

Bias and high-dimensional adjustment in observational studies of peer effects. JASA.
Are observational studies informative about social contagion? We use a massive randomized experiment in Facebook News Feed to identify peer effects in link sharing and compare these results to those using only observational data. The richest models are able to recover the experimental “gold standard” peer effect, at least for the most popular classes of behavior.

Estimating peer effects in networks with peer encouragement designs. PNAS.
We employ a novel experimental design to modulate how many likes and comments are received by 49 million Facebook users, allowing us to estimate network effects in adoption and use of communication technologies.