Timezone: »
Inferring key unobservable features of individuals is an important task in the applied sciences. In particular, an important source of data in fields such as marketing, social sciences and medicine is questionnaires: answers in such questionnaires are noisy measures of target unobserved features. While comprehensive surveys help to better estimate the latent variables of interest, aiming at a high number of questions comes at a price: refusal to participate in surveys can go up, as well as the rate of missing data; quality of answers can decline; costs associated with applying such questionnaires can also increase. In this paper, we cast the problem of refining existing models for questionnaire data as follows: solve a constrained optimization problem of preserving the maximum amount of information found in a latent variable model using only a subset of existing questions. The goal is to find an optimal subset of a given size. For that, we first define an information theoretical measure for quantifying the quality of a reduced questionnaire. Three different approximate inference methods are introduced to solve this problem. Comparisons against a simple but powerful heuristic are presented.
Author Information
Ricardo Silva (University College London)
More from the Same Authors
-
2022 : Pragmatic Fairness: Optimizing Policies with Outcome Disparity Control »
Limor Gultchin · Siyuan Guo · Alan Malek · Silvia Chiappa · Ricardo Silva -
2022 : Evaluating the Impact of Geometric and Statistical Skews on Out-Of-Distribution Generalization Performance »
Aengus Lynch · Jean Kaddour · Ricardo Silva -
2022 : Evaluating the Impact of Geometric and Statistical Skews on Out-Of-Distribution Generalization Performance »
Aengus Lynch · Jean Kaddour · Ricardo Silva -
2022 : Partial identification without distributional assumptions »
Kirtan Padh · Jakob Zeitler · David Watson · Matt Kusner · Ricardo Silva · Niki Kilbertus -
2022 Poster: When Do Flat Minima Optimizers Work? »
Jean Kaddour · Linqing Liu · Ricardo Silva · Matt Kusner -
2021 : Ricardo Silva - The Road to Causal Programming »
Ricardo Silva -
2021 Poster: Causal Effect Inference for Structured Treatments »
Jean Kaddour · Yuchen Zhu · Qi Liu · Matt Kusner · Ricardo Silva -
2020 : Invited Talk: On Prediction, Action and Interference »
Ricardo Silva -
2020 Poster: A Class of Algorithms for General Instrumental Variable Models »
Niki Kilbertus · Matt Kusner · Ricardo Silva -
2018 Poster: Bayesian Semi-supervised Learning with Graph Gaussian Processes »
Yin Cheng Ng · Nicolò Colombo · Ricardo Silva -
2017 Workshop: From 'What If?' To 'What Next?' : Causal Inference and Machine Learning for Intelligent Decision Making »
Ricardo Silva · Panagiotis Toulis · John Shawe-Taylor · Alexander Volfovsky · Thorsten Joachims · Lihong Li · Nathan Kallus · Adith Swaminathan -
2017 Poster: Counterfactual Fairness »
Matt Kusner · Joshua Loftus · Chris Russell · Ricardo Silva -
2017 Oral: Counterfactual Fairness »
Matt Kusner · Joshua Loftus · Chris Russell · Ricardo Silva -
2017 Poster: Tomography of the London Underground: a Scalable Model for Origin-Destination Data »
Nicolò Colombo · Ricardo Silva · Soong Moon Kang -
2017 Poster: When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness »
Chris Russell · Matt Kusner · Joshua Loftus · Ricardo Silva -
2016 Workshop: "What If?" Inference and Learning of Hypothetical and Counterfactual Interventions in Complex Systems »
Ricardo Silva · John Shawe-Taylor · Adith Swaminathan · Thorsten Joachims -
2016 Poster: Observational-Interventional Priors for Dose-Response Learning »
Ricardo Silva -
2016 Poster: Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages »
Yin Cheng Ng · Pawel M Chilinski · Ricardo Silva -
2014 Poster: Causal Inference through a Witness Protection Program »
Ricardo Silva · Robin Evans -
2013 Poster: Flexible sampling of discrete data correlations without the marginal distributions »
Alfredo Kalaitzis · Ricardo Silva -
2007 Poster: Hidden Common Cause Relations in Relational Learning »
Ricardo Silva · Wei Chu · Zoubin Ghahramani -
2007 Spotlight: Hidden Common Cause Relations in Relational Learning »
Ricardo Silva · Wei Chu · Zoubin Ghahramani