CausalSE applies SCMs and propensity score matching to reveal that causal analysis of prompt engineering on GPT-3 code generation often finds no significant effect where associational analysis suggests improvement.
Causality from bottom to top: a survey
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Causal-Adapter adapts frozen diffusion backbones via structural causal modeling, prompt-aligned injection, and conditioned token contrastive loss to achieve faithful counterfactual generation with strong attribute control and identity preservation.
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Rethinking Software Empirical Studies with Structural Causal Models
CausalSE applies SCMs and propensity score matching to reveal that causal analysis of prompt engineering on GPT-3 code generation often finds no significant effect where associational analysis suggests improvement.
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Causal-Adapter: Taming Text-to-Image Diffusion for Faithful Counterfactual Generation
Causal-Adapter adapts frozen diffusion backbones via structural causal modeling, prompt-aligned injection, and conditioned token contrastive loss to achieve faithful counterfactual generation with strong attribute control and identity preservation.