6 papers
Causal inference for group-contaminated structured outcomes: observable quotients, lossless reduction and exact randomization inference
Usef Faghihi, Amir Saki
Structured potential outcomes such as microscopy images may be recorded after an unknown, unit-specific transformation. If that transformation can depend on treatment, covariates o…
Beyond Means: Topological Causal Effects under Persistent-Homology Ignorability
Amir Saki, Usef Faghihi
Average treatment effects (ATE) and conditional average treatment effects (CATE) are foundational causal estimands, but they target changes in expected outcomes and can miss treatm…
Topological Ignorability for Structural Causal Effects Beyond Means
Usef Faghihi
Many interventions alter the structure of an outcome distribution rather than its mean: they can split a population into disconnected regimes, create loops or holes, generate branc…
Global and Local Topology-Aware Attention with Persistent Homology and Euler Biases for Time-Series Forecasting
Usef Faghihi, Amir Saki
Scientific time series often encode predictive geometric structure, including connectivity, cycles, shell-like geometry, directional changes, and nonlinear neighborhoods, that stan…
Causal Deep Q Network
Elouanes Khelifi, Amir Saki, Usef Faghihi
Deep Q Networks (DQN) have shown remarkable success in various reinforcement learning tasks. However, their reliance on associative learning often leads to the acquisition of spuri…
Probabilistic Variational Causal Approach in Observational Studies
Usef Faghihi, Amir Saki
In this paper, we introduce a new causal methodology that accounts for the rarity and frequency of events in observational studies based on their relevance to the underlying proble…