# Empirically Calibrated Conditional Independence Tests

arXiv: https://arxiv.org/abs/2602.21036 · HTML: https://mlsanity.com/paper/2602.21036/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 40 references that are in the map.
- First posted on arXiv 24 Feb 2026.

## Builds on (most cited first, 14 of 14)

- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Panning for Gold: Model-X Knockoffs for High-dimensional Controlled Variable Selection](https://mlsanity.com/paper/1610.02351/) (2016)
- [Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discovery](https://mlsanity.com/paper/1702.03877/) (2017)
- [Model-Powered Conditional Independence Test](https://mlsanity.com/paper/1709.06138/) (2017)
- [Local permutation tests for conditional independence](https://mlsanity.com/paper/2112.11666/) (2021)
- [Deep Direct Likelihood Knockoffs](https://mlsanity.com/paper/2007.15835/) (2020)
- [Estimating and Controlling the False Discovery Rate for the PC Algorithm Using Edge-Specific P-Values](https://mlsanity.com/paper/1607.03975/) (2016)
- [Causal Inference in Genetic Trio Studies](https://mlsanity.com/paper/2002.09644/) (2020)
- [Doubly Robust Conditional Independence Testing with Generative Neural Networks](https://mlsanity.com/paper/2407.17694/) (2024)
- [DIET: Conditional independence testing with marginal dependence measures of residual information](https://mlsanity.com/paper/2208.08579/) (2022)
- [Reconciling model-X and doubly robust approaches to conditional independence testing](https://mlsanity.com/paper/2211.14698/) (2022)
- [Maxway CRT: Improving the Robustness of the Model-X Inference](https://mlsanity.com/paper/2203.06496/) (2022)
- [Covariate Shift Corrected Conditional Randomization Test](https://mlsanity.com/paper/2405.19231/) (2024)
- [Treatment response as a latent variable](https://mlsanity.com/paper/2502.08776/) (2025)

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

- [When a High Score Is an Illusion: Certifying Genuine versus Repackaged Forecasting Skill](https://mlsanity.com/paper/2609.19223/) (2026)

## Abstract (arXiv)

Conditional independence tests (CIT) are widely used for causal discovery and feature selection. Even with false discovery rate (FDR) control procedures, they often fail to provide frequentist guarantees in practice. We highlight two common failure modes: (i) in small samples, asymptotic guarantees for many CITs can be inaccurate and even correctly specified models fail to estimate the noise levels and control the error, and (ii) when sample sizes are large but models are misspecified, unaccounted dependencies skew the test's behavior and fail to return uniform p-values under the null. We propose Empirically Calibrated Conditional Independence Tests (ECCIT), a method that measures and corrects for miscalibration. For a chosen base CIT (e.g., GCM, HRT), ECCIT optimizes an adversary that selects features and response functions to maximize a miscalibration metric. ECCIT then fits a monotone calibration map that adjusts the base-test p-values in proportion to the observed miscalibration. Across empirical benchmarks on synthetic and real data, ECCIT achieves valid FDR with higher power than existing calibration strategies while remaining test agnostic.