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Found 21 papers, 6 papers with code
Doubly Robust Inference in Causal Latent Factor Models
This article introduces a new estimator of average treatment effects under unobserved confounding in modern data-rich environments featuring large numbers of units and outcomes.
Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration
We consider the setting of synthetic control methods (SCMs), a canonical approach used to estimate the treatment effect on the treated in a panel data setting.
Estimating the Value of Evidence-Based Decision Making
Business/policy decisions are often based on evidence from randomized experiments and observational studies.
Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions
Our goal is to learn unit-specific potential outcomes for any combination of these $p$ interventions, i. e., $N \times 2^p$ causal parameters.
Strategyproof Decision-Making in Panel Data Settings and Beyond
Unlike this classical setting, we permit the units generating the panel data to be strategic, i. e. units may modify their pre-intervention outcomes in order to receive a more desirable intervention.
Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interference
We propose an estimator, Network Synthetic Interventions (NSI), and show that it consistently estimates the mean outcomes for a unit under an arbitrary set of counterfactual treatments for the network.
Synthetic Blip Effects: Generalizing Synthetic Controls for the Dynamic Treatment Regime
Our work avoids the combinatorial explosion in the number of units that would be required by a vanilla application of prior synthetic control and synthetic intervention methods in such dynamic treatment regime settings.
CausalSim: A Causal Framework for Unbiased Trace-Driven Simulation
Key to CausalSim is mapping unbiased trace-driven simulation to a tensor completion problem with extremely sparse observations.
Causal Matrix Completion
In particular, we establish entry-wise, i. e., max-norm, finite-sample consistency and asymptotic normality results for matrix completion with MNAR data.
Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy
We propose a procedure for data cleaning, estimation, and inference with data cleaning-adjusted confidence intervals.
PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators
We consider offline reinforcement learning (RL) with heterogeneous agents under severe data scarcity, i. e., we only observe a single historical trajectory for every agent under an unknown, potentially sub-optimal policy.
Single Image Super-Resolution using Residual Channel Attention Network
In this paper, we propose a deep learning-based approach for the problem, wherein we use a fully convolutional attention network coupled with residual in the residual block (RIR), Residual Channel Attention Block (RCAB), and long and short skip connections.
On Model Identification and Out-of-Sample Prediction of Principal Component Regression: Applications to Synthetic Controls
To the best of our knowledge, our prediction guarantees for the fixed design setting have been elusive in both the high-dimensional error-in-variables and synthetic controls literatures.
On Multivariate Singular Spectrum Analysis and its Variants
We introduce and analyze a variant of multivariate singular spectrum analysis (mSSA), a popular time series method to impute and forecast a multivariate time series.
Synthetic Interventions
Towards this, we present a causal framework, synthetic interventions (SI), to infer these $N \times D$ causal parameters while only observing each of the $N$ units under at most two interventions, independent of $D$.
Two Burning Questions on COVID-19: Did shutting down the economy help? Can we (partially) reopen the economy without risking the second wave?
In essence, the method leverages information from different interventions that have already been enacted across the world and fits it to a policy maker's setting of interest, e. g., to estimate the effect of mobility-restricting interventions on the U. S., we use daily death data from countries that enforced severe mobility restrictions to create a "synthetic low mobility U. S." and predict the counterfactual trajectory of the U. S. if it had indeed applied a similar intervention.
Augmented Q Imitation Learning (AQIL)
The study of unsupervised learning can be generally divided into two categories: imitation learning and reinforcement learning.
tspDB: Time Series Predict DB
Computationally, tspDB is 59-62x and 94-95x faster compared to LSTM and DeepAR in terms of median ML model training time and prediction query latency, respectively.
On Robustness of Principal Component Regression
As an important contribution to the Synthetic Control literature, we establish that an (approximate) linear synthetic control exists in the setting of a generalized factor model; traditionally, the existence of a synthetic control needs to be assumed to exist as an axiom.
Model Agnostic Time Series Analysis via Matrix Estimation
In effect, this generalizes the widely used Singular Spectrum Analysis (SSA) in time series literature, and allows us to establish a rigorous link between time series analysis and matrix estimation.
Online Convex Optimization Using Predictions
Making use of predictions is a crucial, but under-explored, area of online algorithms.
