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Found 14 papers, 4 papers with code
Kernel Single Proxy Control for Deterministic Confounding
We consider the problem of causal effect estimation with an unobserved confounder, where we observe a proxy variable that is associated with the confounder.
A Neural Mean Embedding Approach for Back-door and Front-door Adjustment
We consider the estimation of average and counterfactual treatment effects, under two settings: back-door adjustment and front-door adjustment.
Importance Weighting Approach in Kernel Bayes' Rule
We study a nonparametric approach to Bayesian computation via feature means, where the expectation of prior features is updated to yield expected kernel posterior features, based on regression from learned neural net or kernel features of the observations.
Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves
We propose simple nonparametric estimators for mediated and time-varying dose response curves based on kernel ridge regression.
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation
Proxy causal learning (PCL) is a method for estimating the causal effect of treatments on outcomes in the presence of unobserved confounding, using proxies (structured side information) for the confounder.
On Instrumental Variable Regression for Deep Offline Policy Evaluation
By applying different IV techniques to OPE, we are not only able to recover previously proposed OPE methods such as model-based techniques but also to obtain competitive new techniques.
Learning Deep Features in Instrumental Variable Regression
We propose a novel method, deep feature instrumental variable regression (DFIV), to address the case where relations between instruments, treatments, and outcomes may be nonlinear.
Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves
We propose estimators based on kernel ridge regression for nonparametric causal functions such as dose, heterogeneous, and incremental response curves.
Pairwise Supervision Can Provably Elicit a Decision Boundary
A classifier built upon the representations is expected to perform well in downstream classification; however, little theory has been given in literature so far and thereby the relationship between similarity and classification has remained elusive.
Uncoupled Regression from Pairwise Comparison Data
We propose two practical methods for uncoupled regression from pairwise comparison data and show that the learned regression model converges to the optimal model with the optimal parametric convergence rate when the target variable distributes uniformly.
Polynomial-time Algorithms for Multiple-arm Identification with Full-bandit Feedback
Based on our approximation algorithm, we propose novel bandit algorithms for the top-k selection problem, and prove that our algorithms run in polynomial time.
Alternate Estimation of a Classifier and the Class-Prior from Positive and Unlabeled Data
In this paper, we propose a novel unified approach to estimating the class-prior and training a classifier alternately.
Dueling Bandits with Qualitative Feedback
We formulate and study a novel multi-armed bandit problem called the qualitative dueling bandit (QDB) problem, where an agent observes not numeric but qualitative feedback by pulling each arm.
Fully adaptive algorithm for pure exploration in linear bandits
We propose the first fully-adaptive algorithm for pure exploration in linear bandits---the task to find the arm with the largest expected reward, which depends on an unknown parameter linearly.
