Intellectually speaking, a teaching plan helps me gain a better understanding of a subject. Teaching becomes, to a large extent, a byproduct of my need to understand. I would prepare a teaching plan even if I were far from classrooms and students. I can imagine Beethoven writing a string quartet even if he had no hope of ever playing it (not that I’m comparing myself to Beethoven, of course).

Personally, experiencing the students’ reactions—and engaging with them in a discussion that I’ve framed and they often reframe—is both invigorating and humbling. The learning process becomes a two-way road. I have an opportunity to contribute to the students’ growth, and perhaps even to influence their worldview, but I also learn new perspectives that force me to reexamine key ideas and assumptions.

An activity triggered by my urge to understand becomes a collective learning enterprise that I lead but do not rule.

I teach methodology for drawing causal inferences from data. My teaching explores the conditions required to make causal inferences and the methods—study design and data analysis—that can be used to make causal inferences when those conditions are met.

The subject-matter of my courses is that of medicine and public health. My methodological teaching, however, is relevant for any discipline that requires causal inferences, including economics, sociology, law, public policy…

My goal is helping students evaluate the available evidence and generate new new evidence to assist decision-makers (patients, clinicians, regulators and other policy-makers, etc.) Therefore, as I say above, my courses emphasize critical thinking, problem-solving skills, and the ability to articulate sound arguments. Several courses also cover statistical methods for causal inference.

I try to decompose complex issues into simple steps. Teaching is then like climbing a mountain with the students. Each step is easy and, at the end of the process, students will enjoy the view from atop. Frequent reminders about the final prize, as well as some humor and suspense in the story-telling, help students stay by your side long enough to reach the destination.

Lectures are, in my view, an efficient way to present a new topic. Ensuing small group deliberations strengthen and refine understanding. I often assign challenging problems and encourage students to work in groups. In data analysis courses with a hands-on approach, students bring their computers for practical exercises.

In longer courses, we discuss complex real-world problems. Students express their views and use available data to justify them. When dissenting views arise, I help students sharpen their arguments and identify the assumptions that result in different interpretations of the evidence.