Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation
via Incentivized Exploration
- URL: http://arxiv.org/abs/2312.16307v2
- Date: Tue, 13 Feb 2024 22:45:01 GMT
- Title: Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation
via Incentivized Exploration
- Authors: Daniel Ngo, Keegan Harris, Anish Agarwal, Vasilis Syrgkanis, Zhiwei
Steven Wu
- Abstract summary: We shed light on a frequently overlooked but ubiquitous assumption made in synthetic control methods (SCMs) of "overlap"
We propose a framework which incentivizes units with different preferences to take interventions they would not normally consider.
We extend our results to the setting of synthetic interventions, where the goal is to produce counterfactual outcomes under all interventions, not just control.
- Score: 43.59040957749326
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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. We shed light on a frequently overlooked but ubiquitous assumption
made in SCMs of "overlap": a treated unit can be written as some combination --
typically, convex or linear combination -- of the units that remain under
control. We show that if units select their own interventions, and there is
sufficiently large heterogeneity between units that prefer different
interventions, overlap will not hold. We address this issue by proposing a
framework which incentivizes units with different preferences to take
interventions they would not normally consider. Specifically, leveraging tools
from information design and online learning, we propose a SCM that incentivizes
exploration in panel data settings by providing incentive-compatible
intervention recommendations to units. We establish this estimator obtains
valid counterfactual estimates without the need for an a priori overlap
assumption. We extend our results to the setting of synthetic interventions,
where the goal is to produce counterfactual outcomes under all interventions,
not just control. Finally, we provide two hypothesis tests for determining
whether unit overlap holds for a given panel dataset.
Related papers
- Estimating Treatment Effects under Recommender Interference: A Structured Neural Networks Approach [13.208141830901845]
We show that the standard difference-in-means estimator can lead to biased estimates due to recommender interference.
We propose a "recommender choice model" that describes which item gets exposed from a pool containing both treated and control items.
We show that the proposed estimator yields results comparable to the benchmark, whereas the standard difference-in-means estimator can exhibit significant bias and even produce reversed signs.
arXiv Detail & Related papers (2024-06-20T14:53:26Z) - Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm [14.980926991441345]
We show that datasets containing interventional data can be effectively extracted under realistic assumptions about the data distribution.
We introduce interventional faithfulness, which relies on comparisons between the marginal distributions of each variable across observational and interventional settings.
We also introduce Intersort, an algorithm designed to infer the causal order from datasets containing large numbers of single-variable interventions.
arXiv Detail & Related papers (2024-05-28T16:07:17Z) - Differentially Private Synthetic Control [13.320917259299652]
We provide the first algorithms for differentially private synthetic control with explicit error bounds.
We show that our algorithms produce accurate predictions for the target unit, and that the cost of privacy is small.
arXiv Detail & Related papers (2023-03-24T15:49:29Z) - Open-Set Likelihood Maximization for Few-Shot Learning [36.97433312193586]
We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have a few labeled samples.
We explore the popular transductive setting, which leverages the unlabelled query instances at inference.
Motivated by the observation that existing transductive methods perform poorly in open-set scenarios, we propose a generalization of the maximum likelihood principle.
arXiv Detail & Related papers (2023-01-20T01:56:19Z) - Neighborhood Adaptive Estimators for Causal Inference under Network
Interference [152.4519491244279]
We consider the violation of the classical no-interference assumption, meaning that the treatment of one individuals might affect the outcomes of another.
To make interference tractable, we consider a known network that describes how interference may travel.
We study estimators for the average direct treatment effect on the treated in such a setting.
arXiv Detail & Related papers (2022-12-07T14:53:47Z) - Counterfactual inference for sequential experiments [12.900489038342409]
We consider after-study statistical inference for sequentially designed experiments wherein multiple units are assigned treatments for multiple time points.
Our goal is to provide inference guarantees for the counterfactual mean at the smallest possible scale.
We illustrate our theory via several simulations and a case study involving data from a mobile health clinical trial HeartSteps.
arXiv Detail & Related papers (2022-02-14T17:24:27Z) - Deconfounding Scores: Feature Representations for Causal Effect
Estimation with Weak Overlap [140.98628848491146]
We introduce deconfounding scores, which induce better overlap without biasing the target of estimation.
We show that deconfounding scores satisfy a zero-covariance condition that is identifiable in observed data.
In particular, we show that this technique could be an attractive alternative to standard regularizations.
arXiv Detail & Related papers (2021-04-12T18:50:11Z) - Estimating the Effects of Continuous-valued Interventions using
Generative Adversarial Networks [103.14809802212535]
We build on the generative adversarial networks (GANs) framework to address the problem of estimating the effect of continuous-valued interventions.
Our model, SCIGAN, is flexible and capable of simultaneously estimating counterfactual outcomes for several different continuous interventions.
To address the challenges presented by shifting to continuous interventions, we propose a novel architecture for our discriminator.
arXiv Detail & Related papers (2020-02-27T18:46:21Z) - Generalization Bounds and Representation Learning for Estimation of
Potential Outcomes and Causal Effects [61.03579766573421]
We study estimation of individual-level causal effects, such as a single patient's response to alternative medication.
We devise representation learning algorithms that minimize our bound, by regularizing the representation's induced treatment group distance.
We extend these algorithms to simultaneously learn a weighted representation to further reduce treatment group distances.
arXiv Detail & Related papers (2020-01-21T10:16:33Z) - Learning Overlapping Representations for the Estimation of
Individualized Treatment Effects [97.42686600929211]
Estimating the likely outcome of alternatives from observational data is a challenging problem.
We show that algorithms that learn domain-invariant representations of inputs are often inappropriate.
We develop a deep kernel regression algorithm and posterior regularization framework that substantially outperforms the state-of-the-art on a variety of benchmarks data sets.
arXiv Detail & Related papers (2020-01-14T12:56:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.