[Submitted on 8 Oct 2026]
Title:Market Impact Estimation via Causal Foundation Models
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Abstract:When a trader executes a large order, the relevant question is not \emph{what will the price be?} but \emph{what would the price have been had I not traded?} Market impact is thus a causal quantity, yet classical models (Almgren--Chriss, propagators) regress price on flow, which is biased whenever informed flow moves prices through information as well as trading. We present a proof-of-concept \emph{Causal Market Simulator}: a continuous-time causal prior-data fitted network (PFN) trained on a synthetic market prior in which a hidden informational flow confounds the flow--price relation by construction, with confounding strength an exact per-episode knob. On this prior, the naive estimator's bias follows a closed form we confirm empirically (up to 68\% overestimation), while the PFN tracks the unbiased true-parameter oracle at high confounding. Its \emph{do-ablated twin}, the same model with the intervention inputs removed, collapses onto the pre-window mean. Zero-shot, the model responds at the ground-truth scale to metaorders in an ABIDES limit-order book ($+0.38$ pre-onset standard deviations against seed-replay counterfactuals), and its intervention response replicates on about 2,300 detected flow bursts in BTC/ETH/SOL perpetuals and 3,800 on Nasdaq equities, evidence consistent with, though not proof of, causal transfer. Off-prior the response degrades gracefully, and split-conformal recalibration repairs interval calibration. Transfer hinges on encoding the metaorder as added flow rather than a clamped price level, not on more training. A recurrent state-space variant closes the remaining shape gap: its impact accrues over execution duration where the transformer's stays flat, raising the correlation with measured metaorder impact from $+0.68$ to between $+0.92$ and $+0.96$, at the cost of weaker on-prior deconfounding that traces to the training recipe rather than to recurrence.
Comments: In 7th ACM International Conference on AI in Finance (ICAIF '26), November 14-17, 2026, Milan, Italy
Subjects:
Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE); Trading and Market Microstructure (q-fin.TR)
Cite as: arXiv:2610.12537 [cs.LG]
(or arXiv:2610.12537v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.12537
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Dennis Thumm [view email] [v1] Thu, 8 Oct 2026 13:29:39 UTC (182 KB)
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