Generating Counterfactual Explanations Under Temporal Constraints
Authors: Andrei Buliga, Chiara Di Francescomarino, Chiara Ghidini, Marco Montali, Massimiliano Ronzani
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The empirical evaluation shows that the generated counterfactuals are temporally meaningful and more interpretable for applications involving temporal dependencies. An empirical evaluation on real-world and synthetic datasets demonstrates the effectiveness of our approach. |
| Researcher Affiliation | Academia | 1Fondazione Bruno Kessler, Via Sommarive, 18, POVO 38123, Trento, Italy 2Free University of Bozen-Bolzano, via Bruno Buozzi, 1 39100, Bozen-Bolzano, Italy 3University of Trento, Via Sommarive, 9, 38123 Trento, Italy |
| Pseudocode | Yes | Algorithm 1: Temporal Knowledge-aware Crossover operation Algorithm 2: Compute Safe Activities Algorithm 3: Temporal Knowledge-aware Mutation operator |
| Open Source Code | Yes | Code https://github.com/abuliga/AAAI2025-temporalconstrained-counterfactuals |
| Open Datasets | Yes | Experiments are conducted using three datasets commonly used in Process Mining, with details reported in Table 1: Claim Management (Rizzi, Di Francescomarino, and Maggi 2020) is a synthetic dataset pertaining to a claim management process, where accepted claims are labelled as true and rejected claims as false; BPIC2012 (van Dongen 2012) and BPIC2017 (van Dongen 2012) two real-life datasets about a loan application process, where traces with accepted loan offers are labelled as true, and declined offers as false. |
| Dataset Splits | Yes | For each dataset, LTLp formula φ, and prefix length, we split the data into 70% 10% 20% into training, validation, and testing, using a chronological order split. |
| Hardware Specification | Yes | Experiments were run on a M1 with 16GB RAM. |
| Software Dependencies | No | The paper mentions training an XGBoost model but does not specify the version of XGBoost or any other key software libraries used. |
| Experiment Setup | Yes | Regarding the coefficients in Eq. (7), after testing multiple configurations, the final configuration was set to α = 0.5, β = 0.5, γ = 0.5, δ = 0.5 to give all objectives the same weight. For the GA setting, we initialise the population through a hybrid approach: selecting close points from the reference population or, if unavailable, by randomly generating traces. We set the number of generations to 100, pc = 0.5, pmut = 0.2. In population selection, the top 50% of the population, w.r.t. the fitness function, moves to the next generation. Termination occurs at the max generation number or if no significant performance improvement occurs. |