Search Results for author:
Found 11 papers, 3 papers with code
Learned Causal Method Prediction
In this work, we propose CAusal Method Predictor (CAMP), a framework for predicting the best method for a given dataset.
Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs
Causal discovery is crucial for causal inference in observational studies: it can enable the identification of valid adjustment sets (VAS) for unbiased effect estimation.
Local Causal Discovery for Estimating Causal Effects
Even when the causal graph underlying our data is unknown, we can use observational data to narrow down the possible values that an average treatment effect (ATE) can take by (1) identifying the graph up to a Markov equivalence class; and (2) estimating that ATE for each graph in the class.
Discovering Optimal Scoring Mechanisms in Causal Strategic Prediction
While earlier works cast these manipulations as undesirable gaming, recent works have adopted a more nuanced causal framing in which manipulations can improve outcomes of interest, and setting coherent mechanisms requires accounting for both predictive accuracy and improvement of the outcome.
Eulerian Single-Photon Vision
Previous work has largely focused on solving the image reconstruction problem first and then using off-the-shelf methods for downstream tasks, but the most general solutions that account for motion are costly and not scalable to large data volumes produced by single-photon sensors.
Efficient Online Estimation of Causal Effects by Deciding What to Observe
Researchers often face data fusion problems, where multiple data sources are available, each capturing a distinct subset of variables.
Correcting Exposure Bias for Link Recommendation
Link prediction methods are frequently applied in recommender systems, e. g., to suggest citations for academic papers or friends in social networks.
Passive Inter-Photon Imaging
Digital camera pixels measure image intensities by converting incident light energy into an analog electrical current, and then digitizing it into a fixed-width binary representation.
Quanta Burst Photography
These single-photon cameras (SPCs) are capable of capturing high-speed sequences of binary single-photon images with no read noise.
Estimating Treatment Effects with Observed Confounders and Mediators
We show that it strictly outperforms the backdoor and frontdoor estimators and that this improvement can be unbounded.
Neural Signatures for Licence Plate Re-identification
The template database is restricted to contain only a single signature per unique licence plate for our problem.
