12 papers · 1 filter
Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration
Amir Asiaee, Kaveh Aryan
Workload-based differentially private (DP) synthetic data methods privately measure aggregate queries and post-process the noisy answers into synthetic records. Generic workloads c…
Certified Interventional Fidelity: Anytime-Valid, Adaptive Evaluation of Causal Claims in Mechanistic Interpretability
Amir Asiaee
Mechanistic interpretability often evaluates explanations by intervening on a model: swapping hidden states, patching activations, ablating components, or comparing a compressed mo…
CausalWrap: Model-Agnostic Causal Constraint Wrappers for Tabular Synthetic Data
Amir Asiaee, Zhuohui J. Liang, Chao Yan
Tabular synthetic data generators are typically trained to match observational distributions, which can yield high conventional utility (e.g., column correlations, predictive accur…
Noise-Calibrated Inference from Differentially Private Sufficient Statistics in Exponential Families
Amir Asiaee, Samhita Pal
Many differentially private (DP) data release systems either output DP synthetic data and leave analysts to perform inference as usual, which can lead to severe miscalibration, or…
Improving RCT-Based Treatment Effect Estimation Under Covariate Mismatch via Calibrated Alignment
Amir Asiaee, Samhita Pal
Randomized controlled trials (RCTs) are the gold standard for estimating treatment effects, yet they are often underpowered for detecting effect heterogeneity. Large observational…
Partial Causal Structure Learning for Valid Selective Conformal Inference under Interventions
Amir Asiaee, Kavey Aryan, James P. Long
Selective conformal prediction can yield substantially tighter uncertainty sets when we can identify calibration examples that are exchangeable with the test example. In interventi…
Projected Boosting with Fairness Constraints: Quantifying the Cost of Fair Training Distributions
Amir Asiaee, Kaveh Aryan
Boosting algorithms enjoy strong theoretical guarantees: when weak learners maintain positive edge, AdaBoost achieves geometric decrease of exponential loss. We study how to incorp…
Fix Representation (Optimally) Before Fairness: Finite-Sample Shrinkage Population Correction and the True Price of Fairness Under Subpopulation Shift
Amir Asiaee, Kaveh Aryan
Machine learning practitioners frequently observe tension between predictive accuracy and group fairness constraints -- yet sometimes fairness interventions appear to improve accur…
Fairness Under Group-Conditional Prior Probability Shift: Invariance, Drift, and Target-Aware Post-Processing
Amir Asiaee, Kaveh Aryan
Machine learning systems are often trained and evaluated for fairness on historical data, yet deployed in environments where conditions have shifted. A particularly common form of…
Causal Mechanism Reduction: Mechanism Replacement for Neural Network Pruning and Abstraction
Amir Asiaee
Which internal mechanisms of a neural network can be replaced while preserving the computation it performs? Structured pruning asks for smaller deployable networks; causal abstract…
Causal Discovery with Mixed Latent Confounding via Precision Decomposition
Amir Asiaee, Samhita Pal, James O'quinn +1
We study causal discovery from observational data in linear Gaussian systems affected by \emph{mixed latent confounding}, where some unobserved factors act broadly across many vari…
DAG DECORation: Continuous Optimization for Structure Learning under Hidden Confounding
Samhita Pal, James O'quinn, Kaveh Aryan +3
We study structure learning for linear Gaussian SEMs in the presence of latent confounding. Existing continuous methods excel when errors are independent, while deconfounding-first…