On both synthetic and realistic data LOAD
🏎️is more computationally efficient than global methods, performing close to local methods,
💎recovers high-quality, statistically efficient adjustment sets,
🔮thus enables reliable causal effect estimation even at scale

7/8

To do this, we develop a sufficient and necessary test for the identifiability of the causal effect of a treatment on an outcome using only local information around the treatment and its siblings, no matter how far the treatment and the outcome are in the causal graph 🔭

5/8

🌐 Global discovery methods can find optimal adjustment sets, but at a huge computational cost.
📍 Local discovery methods are fast, but can only find sub-optimal adjustment sets.

Can we get the best of both worlds and find optimal adjustment sets from local information?

3/8

While all valid adjustment sets enable unbiased estimation of causal effects, using the optimal adjustment set in terms of asymptotic variance is crucial for reliable causal effect estimation!⚠️

But how to find the optimal adjustment set if the causal graph is not available?

2/8

Estimating causal effects efficiently doesn’t have to mean discovering the entire causal graph! Now you can find the optimal adjustment from only local information using LOAD!

📜 Preprint: arxiv.org/abs/2510.14582
👾 Code: github.com/Matyasch/load
🧵 1/8

A little over a week ago, I had the chance to attend #AISTATS and present our poster on SNAP (matyasch.github.io/snap)! Three days of brilliant invited talks and a stream of fascinating papers left me with a much longer reading list about ideas to explore.

Just arrived in Phuket for #AISTATS2025. Can't wait to present our poster (in tube) about SNAP 🫰 on day 2, Sunday! Come check it out and let's chat about scalable causal discovery!

A few weeks ago, I presented SNAP at the wonderful #Bellairs Workshop on Causality in Barbados🐢

This Friday 🫰meets 🤌 as I will get to present SNAP again at the kick-off of the newest season of @causalclub.di.unipi.it! Check out this, and their other amazing upcoming talks at causalclub.di.unipi.it

9/10 We also evaluate SNAP on semi-synthetic settings including data generated from the MAGIC-NIAB network, which captures genetic effects and phenotypic interactions 🧬 We see that SNAP greatly reduces the number of CI tests and execution time compared to most baselines.

8/10 Many non-ancestors are already identified by marginal tests, enabling prefiltering with SNAP(0) to significantly speed up computation time. Increasing the number of prefiltering iterations k further reduces the number of CI tests needed, especially in dense graphs 🧶

7/10 SNAP(∞) consistently ranks among the best in the number of CI tests and computation time across all domains, while maintaining a comparable intervention distance. In contrast, other methods vary in performance depending on the setting 🚀

6/10 We can also run SNAP until completion, to obtain a stand-alone causal discovery algorithm, called SNAP(∞). SNAP(∞) is sound and complete over the possible ancestors of targets ✅ Thus, unlike previous work on local causal discovery, it finds efficient adjustment sets.

4/10 To solve this task, we show that only possible ancestors of the targets are required to identify their causal relationships and efficient adjustment sets💡 Driven by this, we propose SNAP to progressively prune non-ancestors, leading to much fewer higher order CI tests.

Do you want to estimate causal effects for a small set of target variables without knowing the causal graph, but discovering it takes too long? Now you can get adjustment sets in a SNAP🫰accepted at #aistats2025!

📜 arxiv.org/abs/2502.07857
🧩 matyasch.github.io/snap/
🧵 1/10