Efficient Discovery of Heterogeneous Quantile Treatment Effects in
Randomized Experiments via Anomalous Pattern Detection
- URL: http://arxiv.org/abs/1803.09159v3
- Date: Wed, 10 May 2023 18:42:11 GMT
- Title: Efficient Discovery of Heterogeneous Quantile Treatment Effects in
Randomized Experiments via Anomalous Pattern Detection
- Authors: Edward McFowland III, Sriram Somanchi, Daniel B. Neill
- Abstract summary: Treatment Effect Subset Scan (TESS) is a new method for discovering which subpopulation in a randomized experiment is most significantly affected by a treatment.
In addition to the algorithm, we demonstrate that under the sharp null hypothesis of no treatment effect, the Type I and II error can be controlled.
- Score: 1.9346186297861747
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the recent literature on estimating heterogeneous treatment effects, each
proposed method makes its own set of restrictive assumptions about the
intervention's effects and which subpopulations to explicitly estimate.
Moreover, the majority of the literature provides no mechanism to identify
which subpopulations are the most affected--beyond manual inspection--and
provides little guarantee on the correctness of the identified subpopulations.
Therefore, we propose Treatment Effect Subset Scan (TESS), a new method for
discovering which subpopulation in a randomized experiment is most
significantly affected by a treatment. We frame this challenge as a pattern
detection problem where we efficiently maximize a nonparametric scan statistic
(a measure of the conditional quantile treatment effect) over subpopulations.
Furthermore, we identify the subpopulation which experiences the largest
distributional change as a result of the intervention, while making minimal
assumptions about the intervention's effects or the underlying data generating
process. In addition to the algorithm, we demonstrate that under the sharp null
hypothesis of no treatment effect, the asymptotic Type I and II error can be
controlled, and provide sufficient conditions for detection consistency--i.e.,
exact identification of the affected subpopulation. Finally, we validate the
efficacy of the method by discovering heterogeneous treatment effects in
simulations and in real-world data from a well-known program evaluation study.
Related papers
- Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner [72.20769640318969]
Estimating causal quantities from observational data is crucial for understanding the safety and effectiveness of medical treatments.
Medical practitioners require not only estimating averaged causal quantities, but also understanding the randomness of the treatment effect as a random variable.
This randomness is referred to as aleatoric uncertainty and is necessary for understanding the probability of benefit from treatment or quantiles of the treatment effect.
arXiv Detail & Related papers (2024-11-05T18:14:49Z) - Assumption-Lean Post-Integrated Inference with Negative Control Outcomes [0.0]
We introduce a robust post-integrated inference (PII) method that adjusts for latent heterogeneity using negative control outcomes.
Our method extends to projected direct effect estimands, accounting for hidden mediators, confounders, and moderators.
The proposed doubly robust estimators are consistent and efficient under minimal assumptions and potential misspecification.
arXiv Detail & Related papers (2024-10-07T12:52:38Z) - Identification of Average Causal Effects in Confounded Additive Noise Models [7.064432289838905]
We introduce a novel approach for estimating the average causal effects (ACEs) of any subset of the treatment variables on the outcome.
We also propose a randomized algorithm that further reduces the number of required interventions to poly-logarithmic in the number of nodes.
This establishes that a poly-logarithmic number of interventions is sufficient to infer the causal effects of any subset of treatments on the outcome in confounded ANMs with high probability.
arXiv Detail & Related papers (2024-07-13T21:46:57Z) - Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment
Effect Estimation [137.3520153445413]
A notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference.
We evaluate seven established baseline causal discovery methods including a newly proposed method based on GFlowNets.
The results of our study demonstrate that some of the algorithms studied are able to effectively capture a wide range of useful and diverse ATE modes.
arXiv Detail & Related papers (2023-07-11T02:58:10Z) - Sample Constrained Treatment Effect Estimation [28.156207324508706]
We focus on designing efficient randomized controlled trials, to accurately estimate the effect of some treatment on a population of $n$ individuals.
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.
arXiv Detail & Related papers (2022-10-12T21:13:47Z) - Adaptive Identification of Populations with Treatment Benefit in
Clinical Trials: Machine Learning Challenges and Solutions [78.31410227443102]
We study the problem of adaptively identifying patient subpopulations that benefit from a given treatment during a confirmatory clinical trial.
We propose AdaGGI and AdaGCPI, two meta-algorithms for subpopulation construction.
arXiv Detail & Related papers (2022-08-11T14:27:49Z) - Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments [0.9208007322096533]
We develop a general approach to statistical inference for heterogeneous treatment effects discovered by a generic ML algorithm.
We show how to estimate the average treatment effect within each of these groups, and construct a valid confidence interval.
arXiv Detail & Related papers (2022-03-28T05:43:46Z) - To Impute or not to Impute? -- Missing Data in Treatment Effect
Estimation [84.76186111434818]
We identify a new missingness mechanism, which we term mixed confounded missingness (MCM), where some missingness determines treatment selection and other missingness is determined by treatment selection.
We show that naively imputing all data leads to poor performing treatment effects models, as the act of imputation effectively removes information necessary to provide unbiased estimates.
Our solution is selective imputation, where we use insights from MCM to inform precisely which variables should be imputed and which should not.
arXiv Detail & Related papers (2022-02-04T12:08:31Z) - Treatment Effect Risk: Bounds and Inference [58.442274475425144]
Since the average treatment effect measures the change in social welfare, even if positive, there is a risk of negative effect on, say, some 10% of the population.
In this paper we consider how to nonetheless assess this important risk measure, formalized as the conditional value at risk (CVaR) of the ITE distribution.
Some bounds can also be interpreted as summarizing a complex CATE function into a single metric and are of interest independently of being a bound.
arXiv Detail & Related papers (2022-01-15T17:21:26Z) - Efficient Causal Inference from Combined Observational and
Interventional Data through Causal Reductions [68.6505592770171]
Unobserved confounding is one of the main challenges when estimating causal effects.
We propose a novel causal reduction method that replaces an arbitrary number of possibly high-dimensional latent confounders.
We propose a learning algorithm to estimate the parameterized reduced model jointly from observational and interventional data.
arXiv Detail & Related papers (2021-03-08T14:29:07Z) - Conformal Inference of Counterfactuals and Individual Treatment Effects [6.810856082577402]
We propose a conformal inference-based approach that can produce reliable interval estimates for counterfactuals and individual treatment effects.
Existing methods suffer from a significant coverage deficit even in simple models.
arXiv Detail & Related papers (2020-06-11T01:03:32Z)
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.