DICTUM: Diffusion Counterfactuals for Tabular Mixed-type Data

Counterfactual explanations (CFs) provide actionable recourse by identifying minimal input changes that alter a model’s prediction. Existing methods either optimize a single CF for a fixed criteria or generate multiple CFs from narrow distributions, limiting their ability to adapt to diverse user preferences at inference time. We propose DICTUM, a conditional diffusion framework for tabular data that models a broad distribution over the counterfactual space by training on opposite-class neighbors selected via a Determinantal Point Process (DPP). This diverse training signal, combined with a Transformer-based mixed-type dif- fusion backbone featuring per-column learnable noise schedules, enables DICTUM to generate counterfactual sets with substantially higher distributional coverage than prior generative approaches. At inference time, a branching generation strategy, inspired by SVDD-style guidance, enforces user-specified actionability constraints on the learned distribution, without retraining. Experiments on five tabular benchmarks show that DICTUM achieves competitive or superior performance across validity, proximity, sparsity, plausibility, and diversity metrics. A constraint-strictness sweep and a component ablation further confirm that quality and plausibility are preserved as constraints tighten.