Search Results for author:
Found 20 papers, 8 papers with code
Continuous Treatment Effects with Surrogate Outcomes
Incorporating surrogates, which are fully observed post-treatment variables related to the primary outcome, can improve estimation in this case.
Leveraging Graph Diffusion Models for Network Refinement Tasks
Most real-world networks are noisy and incomplete samples from an unknown target distribution.
Distributional Off-Policy Evaluation for Slate Recommendations
Prior work has developed estimators that leverage the structure in slates to estimate the expected off-policy performance, but the estimation of the entire performance distribution remains elusive.
Sample Constrained Treatment Effect Estimation
In particular, we study sample-constrained treatment effect estimation, where we must select a subset of $s \ll n$ individuals from the population to experiment on.
Learning Relational Causal Models with Cycles through Relational Acyclification
We introduce relational acyclification, an operation specifically designed for relational models that enables reasoning about the identifiability of cyclic relational causal models.
Non-Parametric Inference of Relational Dependence
Independence testing plays a central role in statistical and causal inference from observational data.
Offline Evaluation of Ranked Lists using Parametric Estimation of Propensities
Search engines and recommendation systems attempt to continually improve the quality of the experience they afford to their users.
Off-Policy Evaluation in Embedded Spaces
Second, many recommendation systems are not probabilistic and so having access to logging and target policy densities may not be feasible.
Relational Causal Models with Cycles:Representation and Reasoning
To facilitate cycles in relational representation and learning, we introduce relational $\sigma$-separation, a new criterion for understanding relational systems with feedback loops.
Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning
Online reinforcement learning (RL) algorithms are often difficult to deploy in complex human-facing applications as they may learn slowly and have poor early performance.
Time-uniform central limit theory and asymptotic confidence sequences
This paper introduces time-uniform analogues of such asymptotic confidence intervals, adding to the literature on confidence sequences (CS) -- sequences of confidence intervals that are uniformly valid over time -- which provide valid inference at arbitrary stopping times and incur no penalties for "peeking" at the data, unlike classical confidence intervals which require the sample size to be fixed in advance.
Online Discrepancy Minimization via Persistent Self-Balancing Walks
We study the online discrepancy minimization problem for vectors in $\mathbb{R}^d$ in the oblivious setting where an adversary is allowed fix the vectors $x_1, x_2, \ldots, x_n$ in arbitrary order ahead of time.
Data Structures and Algorithms Discrete Mathematics Combinatorics
Heterogeneous Graphlets
Notably, since typed graphlet is more general than colored graphlet (and untyped graphlets), the counts of various typed graphlets can be combined to obtain the counts of the much simpler notion of colored graphlets.
Efficient Balanced Treatment Assignments for Experimentation
In this work, we reframe the problem of balanced treatment assignment as optimization of a two-sample test between test and control units.
Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inference
Across 648 experiments and two datasets, we evaluate every commonly used causal inference method and identify their strengths and weaknesses to inform social media researchers seeking to use such methods, and guide future improvements.
Designing Transportable Experiments
To reduce the variance of our estimator, we design a covariate balance condition (Target Balance) between the treatment and control groups based on the target population.
Methodology
General Identification of Dynamic Treatment Regimes Under Interference
In many applied fields, researchers are often interested in tailoring treatments to unit-level characteristics in order to optimize an outcome of interest.
Balanced off-policy evaluation in general action spaces
We show that minimizing the risk of the classifier implies minimization of imbalance to the desired counterfactual distribution of state-action pairs.
Heterogeneous Network Motifs
To address this problem, we propose a fast, parallel, and space-efficient framework for counting typed graphlets in large networks.
A Sound and Complete Algorithm for Learning Causal Models from Relational Data
However, there is no equivalent complete algorithm for learning the structure of relational models, a more expressive generalization of Bayesian networks.
