Causal Bandits Podcast Create Your Causal Inference Roadmap. Causal Inference, TMLE & Sensitivity | Mark van der Laan S2E6 | CausalBanditsPodcast.com
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Sep 22, 2025 Mark van der Laan, a renowned professor at UC Berkeley and the mastermind behind Targeted Maximum Likelihood Estimation (TMLE), dives deep into causal inference. He differentiates between TMLE and double machine learning, emphasizing their unique applications. Mark shares insights on building a stepwise causal roadmap and the importance of uncertainty quantification. He discusses practical applications of his work and reflects on the role of large language models in research. His advice encourages diversity and rigor in the causal inference community.
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Use Negative Controls To Bound Bias
- Use negative controls and leave-one-out confounder checks to bound plausible unmeasured confounding.
- Translate those empirical shifts into interpretable units to judge how credible a causal gap is.
Learn What Data Actually Identifies
- Rather than chase an unidentifiable ATE, identify mean outcomes under stochastic interventions on an instrument.
- This maps instrument interventions to learnable treatment regimens, reducing untestable assumptions.
Practical Problems Shaped His Research
- Mark traces his motivation to real-world problems and mentors like Jamie Robins who urged tackling longitudinal complexity.
- That applied drive shaped TMLE, super learner, and his continual focus on practical solutions.
