Real-world uplift modelling:

CausalPFN works out of the box on real-world data. On 5 real RCTs in marketing (Hillstrom, Criteo, Lenta, etc.), it outperforms baselines like X-/S-/DA-Learners on policy evaluation (Qini score). [5/7]

Benchmarks:

On IHDP, ACIC, Lalonde:
– Best avg. rank across many tasks
– Faster than all baselines
– No tuning needed compared to the baselines (that were tuned via cross-validation)
[4/7]

🚨 Introducing CausalPFN, a foundation model trained on simulated data for in-context causal effect estimation, based on prior-fitted networks (PFNs). Joint work with Hamid Kamkari, Layer6AI & @rahulgk.bsky.social 🧵[1/7]

📝 arxiv.org/abs/2506.07918
🔗 github.com/vdblm/Causal...
🗣️Oral@ICML SIM workshop

To do so, we consider all prior distributions on the unobserved factors (e.g. the distribution over each arm's mean reward) that align with the expert data. We then choose the prior with the maximum entropy (least information) and apply posterior sampling to guide the exploration (4/5)

Unobserved confounding factors affect the expert policy in ways that are not understood by the learner. An important example is experts acting with privileged information. Naive imitation leads to single aggregated policies for each observed state and fails to generalize (2/5)

How can we use offline expert data with unobserved confounding to guide exploration in RL? Our approach is to learn prior distributions from expert data and follow posterior sampling

Come to our poster #NeurIPS2024 today to learn more!

🗓️ Thu 12 Dec 4:30 - 7 pm PST
📍 West Ballroom A-D #6708

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