Mátyás Schubert
@matyasch.bsky.social
PhD in causal machine learning @amlab.bsky.social
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
📜 Preprint: arxiv.org/abs/2510.14582
👾 Code: github.com/Matyasch/load
🧵 1/8
October 23, 2025 at 3:21 PM
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
📜 Preprint: arxiv.org/abs/2510.14582
👾 Code: github.com/Matyasch/load
🧵 1/8
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.
February 13, 2025 at 2:01 PM
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.
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
📜 arxiv.org/abs/2502.07857
🧩 matyasch.github.io/snap/
🧵 1/10
February 13, 2025 at 1:59 PM
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
📜 arxiv.org/abs/2502.07857
🧩 matyasch.github.io/snap/
🧵 1/10