[2602.23541] Causal Identification from Counterfactual Data: Completeness and Bounding Results

Computer Science > Artificial Intelligence arXiv:2602.23541 (cs) [Submitted on 26 Feb 2026] Title:Causal Identification from Counterfactual Data: Completeness and Bounding Results Authors:Arvind Raghavan, Elias Bareinboim View a PDF of the paper titled Causal Identification from Counterfactual Data: Completeness and Bounding Results, by Arvind Raghavan and 1 other authors View PDF Abstract:Previous work establishing completeness results for $\textit{counterfactual identification}$ has been circumscribed to the setting where the input data belongs to observational or interventional distributions (Layers 1 and 2 of Pearl's Causal Hierarchy), since it was generally presumed impossible to obtain data from counterfactual distributions, which belong to Layer 3. However, recent work (Raghavan & Bareinboim, 2025) has formally characterized a family of counterfactual distributions which can be directly estimated via experimental methods - a notion they call $\textit{counterfactual realizabilty}$. This leaves open the question of what $\textit{additional}$ counterfactual quantities now become identifiable, given this new access to (some) Layer 3 data. To answer this question, we develop the CTFIDU+ algorithm for identifying counterfactual queries from an arbitrary set of Layer 3 distributions, and prove that it is complete for this task. Building on this, we establish the theoretical limit of which counterfactuals can be identified from physically realizable distributions, thus impl...