Functional effects models: Accounting for preference heterogeneity in panel data with machine learning

• URL: http://arxiv.org/abs/2509.18047v1
• Date: Mon, 22 Sep 2025 17:22:18 GMT
• Title: Functional effects models: Accounting for preference heterogeneity in panel data with machine learning
• Authors: Nicolas Salvadé, Tim Hillel,
• Abstract summary: We present a general specification for Functional Effects Models, which use Machine Learning (ML) methodologies to learn individual-specific preference parameters from socio-demographic characteristics.<n>We identify three specific advantages of the Functional Effects Model over traditional fixed, and random/mixed effects models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we present a general specification for Functional Effects Models, which use Machine Learning (ML) methodologies to learn individual-specific preference parameters from socio-demographic characteristics, therefore accounting for inter-individual heterogeneity in panel choice data. We identify three specific advantages of the Functional Effects Model over traditional fixed, and random/mixed effects models: (i) by mapping individual-specific effects as a function of socio-demographic variables, we can account for these effects when forecasting choices of previously unobserved individuals (ii) the (approximate) maximum-likelihood estimation of functional effects avoids the incidental parameters problem of the fixed effects model, even when the number of observed choices per individual is small; and (iii) we do not rely on the strong distributional assumptions of the random effects model, which may not match reality. We learn functional intercept and functional slopes with powerful non-linear machine learning regressors for tabular data, namely gradient boosting decision trees and deep neural networks. We validate our proposed methodology on a synthetic experiment and three real-world panel case studies, demonstrating that the Functional Effects Model: (i) can identify the true values of individual-specific effects when the data generation process is known; (ii) outperforms both state-of-the-art ML choice modelling techniques that omit individual heterogeneity in terms of predictive performance, as well as traditional static panel choice models in terms of learning inter-individual heterogeneity. The results indicate that the FI-RUMBoost model, which combines the individual-specific constants of the Functional Effects Model with the complex, non-linear utilities of RUMBoost, performs marginally best on large-scale revealed preference panel data.

Related papers

• xtdml: Double Machine Learning Estimation to Static Panel Data Models with Fixed Effects in R [0.0]
  The paper presents the R package xtdml, which implements DML methods for partially linear panel regression models.<n>The package provides functionalities to: (a) learn nuisance functions with machine learning algorithms from the mlr3 ecosystem.<n>We showcase the use of xtdml with both simulated and real longitudinal data.
  arXiv  Detail & Related papers  (2025-12-17T20:48:40Z)
• Nonparametric Data Attribution for Diffusion Models [57.820618036556084]
  Data attribution for generative models seeks to quantify the influence of individual training examples on model outputs.<n>We propose a nonparametric attribution method that operates entirely on data, measuring influence via patch-level similarity between generated and training images.
  arXiv  Detail & Related papers  (2025-10-16T03:37:16Z)
• Towards Understanding Valuable Preference Data for Large Language Model Alignment [85.38864561060088]
  Large language model (LLM) alignment is typically achieved through learning from human preference comparisons.<n>We assess data quality through individual influence on validation data using our newly proposed truncated influence function (TIF)<n>To this end, we combine them to offset their diverse error sources, resulting in a simple yet effective data selection rule.
  arXiv  Detail & Related papers  (2025-10-15T06:57:55Z)
• Detecting and Pruning Prominent but Detrimental Neurons in Large Language Models [68.57424628540907]
  Large language models (LLMs) often develop learned mechanisms specialized to specific datasets.<n>We introduce a fine-tuning approach designed to enhance generalization by identifying and pruning neurons associated with dataset-specific mechanisms.<n>Our method employs Integrated Gradients to quantify each neuron's influence on high-confidence predictions, pinpointing those that disproportionately contribute to dataset-specific performance.
  arXiv  Detail & Related papers  (2025-07-12T08:10:10Z)
• Explainable Artificial Intelligence for Dependent Features: Additive Effects of Collinearity [0.0]
  We propose an Additive Effects of Collinearity (AEC) as a novel XAI method to consider the collinearity issue. The proposed method is implemented using simulated and real data to validate its efficiency comparing with the a state of arts XAI method.
  arXiv  Detail & Related papers  (2024-10-30T07:00:30Z)
• Influence Functions for Scalable Data Attribution in Diffusion Models [52.92223039302037]
  Diffusion models have led to significant advancements in generative modelling.<n>Yet their widespread adoption poses challenges regarding data attribution and interpretability.<n>We develop an influence functions framework to address these challenges.
  arXiv  Detail & Related papers  (2024-10-17T17:59:02Z)
• Outlier Gradient Analysis: Efficiently Identifying Detrimental Training Samples for Deep Learning Models [36.05242956018461]
  In this paper, we establish a bridge between identifying detrimental training samples via influence functions and outlier gradient detection.<n>We first validate the hypothesis of our proposed outlier gradient analysis approach on synthetic datasets.<n>We then demonstrate its effectiveness in detecting mislabeled samples in vision models and selecting data samples for improving performance of natural language processing transformer models.
  arXiv  Detail & Related papers  (2024-05-06T21:34:46Z)
• The Importance of Model Inspection for Better Understanding Performance Characteristics of Graph Neural Networks [15.569758991934934]
  We investigate the effect of modelling choices on the feature learning characteristics of graph neural networks applied to a brain shape classification task. We find substantial differences in the feature embeddings at different layers of the models.
  arXiv  Detail & Related papers  (2024-05-02T13:26:18Z)
• Double Machine Learning for Static Panel Models with Fixed Effects [0.0]
  We develop novel machine learning procedures for panel data.<n>New procedures are extensions of the well-known correlated random effects, within-group and first-difference estimators.<n>We use our procedures to re-estimate the impact of minimum wage on voting behaviour in the UK.
  arXiv  Detail & Related papers  (2023-12-13T14:34:12Z)
• Variable Importance Matching for Causal Inference [73.25504313552516]
  We describe a general framework called Model-to-Match that achieves these goals. Model-to-Match uses variable importance measurements to construct a distance metric. We operationalize the Model-to-Match framework with LASSO.
  arXiv  Detail & Related papers  (2023-02-23T00:43:03Z)
• A prediction and behavioural analysis of machine learning methods for modelling travel mode choice [0.26249027950824505]
  We conduct a systematic comparison of different modelling approaches, across multiple modelling problems, in terms of the key factors likely to affect model choice. Results indicate that the models with the highest disaggregate predictive performance provide poorer estimates of behavioural indicators and aggregate mode shares. It is also observed that the MNL model performs robustly in a variety of situations, though ML techniques can improve the estimates of behavioural indices such as Willingness to Pay.
  arXiv  Detail & Related papers  (2023-01-11T11:10:32Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity [2.8402080392117752]
  neuroscience applications collect high-dimensional predictors' corresponding to brain activity in different regions along with behavioral outcomes. Joint factor models for the predictors and outcomes are natural, but maximum likelihood estimates of these models can struggle in practice when there is model misspecification. We propose an alternative inference strategy based on supervised autoencoders; rather than placing a probability distribution on the latent factors, we define them as an unknown function of the high-dimensional predictors.
  arXiv  Detail & Related papers  (2020-04-10T19:31:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.