# Revealing Unobservables by Deep Learning: Generative Element Extraction Networks (GEEN)

arXiv: https://arxiv.org/abs/2210.01300 · HTML: https://mlsanity.com/paper/2210.01300/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 4 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [MINE: Mutual Information Neural Estimation](https://mlsanity.com/paper/1801.04062/) (2018)
- [f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization](https://mlsanity.com/paper/1606.00709/) (2016)
- [GAIN: Missing Data Imputation using Generative Adversarial Nets](https://mlsanity.com/paper/1806.02920/) (2018)
- [Handling Incomplete Heterogeneous Data using VAEs](https://mlsanity.com/paper/1807.03653/) (2018)
- [Missing Data Imputation using Optimal Transport](https://mlsanity.com/paper/2002.03860/) (2020)
- [MisGAN: Learning from Incomplete Data with Generative Adversarial Networks](https://mlsanity.com/paper/1902.09599/) (2019)

## Built on by (most cited first, 1 of 1)

- [Identification of Unobservables in Observations](https://mlsanity.com/paper/2212.02585/) (2022)

## Abstract (arXiv)

Latent variable models are crucial in scientific research, where a key variable, such as effort, ability, and belief, is unobserved in the sample but needs to be identified. This paper proposes a novel method for estimating realizations of a latent variable X^* in a random sample that contains its multiple measurements. With the key assumption that the measurements are independent conditional on X^*, we provide sufficient conditions under which realizations of X^* in the sample are locally unique in a class of deviations, which allows us to identify realizations of X^*. To the best of our knowledge, this paper is the first to provide such identification in observation. We then use the Kullback-Leibler distance between the two probability densities with and without the conditional independence as the loss function to train a Generative Element Extraction Networks (GEEN) that maps from the observed measurements to realizations of X^* in the sample. The simulation results imply that this proposed estimator works quite well and the estimated values are highly correlated with realizations of X^*. Our estimator can be applied to a large class of latent variable models and we expect it will change how people deal with latent variables.