Local Effect

Local effect analysis focuses on understanding the impact of specific variables or interventions on an outcome, particularly within subsets of data or under specific conditions. Current research emphasizes causal inference methods, including techniques like regression discontinuity design and disentangled architectures, to accurately estimate these effects, even with complex data structures and limited information. These advancements are improving the interpretability of complex models (e.g., black-box machine learning algorithms) and enabling more robust causal analyses across diverse fields, from healthcare to time-series forecasting. The development of methods like Accumulated Local Effects (ALE) and its variants aims to provide more accurate and efficient estimations of local effects, addressing challenges like high dimensionality and data heterogeneity.