Estimating Treatment Effects Under Heterogeneous Interference
- URL: http://arxiv.org/abs/2309.13884v1
- Date: Mon, 25 Sep 2023 05:44:17 GMT
- Title: Estimating Treatment Effects Under Heterogeneous Interference
- Authors: Xiaofeng Lin, Guoxi Zhang, Xiaotian Lu, Han Bao, Koh Takeuchi, Hisashi
Kashima
- Abstract summary: We propose a novel approach to model heterogeneous interference by developing a new architecture to aggregate information from diverse neighbors.
Our proposed method contains graph neural networks that aggregate same-view information, a mechanism that aggregates information from different views, and attention mechanisms.
- Score: 24.714971680893402
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Treatment effect estimation can assist in effective decision-making in
e-commerce, medicine, and education. One popular application of this estimation
lies in the prediction of the impact of a treatment (e.g., a promotion) on an
outcome (e.g., sales) of a particular unit (e.g., an item), known as the
individual treatment effect (ITE). In many online applications, the outcome of
a unit can be affected by the treatments of other units, as units are often
associated, which is referred to as interference. For example, on an online
shopping website, sales of an item will be influenced by an advertisement of
its co-purchased item. Prior studies have attempted to model interference to
estimate the ITE accurately, but they often assume a homogeneous interference,
i.e., relationships between units only have a single view. However, in
real-world applications, interference may be heterogeneous, with multi-view
relationships. For instance, the sale of an item is usually affected by the
treatment of its co-purchased and co-viewed items. We hypothesize that ITE
estimation will be inaccurate if this heterogeneous interference is not
properly modeled. Therefore, we propose a novel approach to model heterogeneous
interference by developing a new architecture to aggregate information from
diverse neighbors. Our proposed method contains graph neural networks that
aggregate same-view information, a mechanism that aggregates information from
different views, and attention mechanisms. In our experiments on multiple
datasets with heterogeneous interference, the proposed method significantly
outperforms existing methods for ITE estimation, confirming the importance of
modeling heterogeneous interference.
Related papers
- Causal Message Passing: A Method for Experiments with Unknown and General Network Interference [5.294604210205507]
We introduce a new framework to accommodate complex and unknown network interference.
Our framework, termed causal message-passing, is grounded in high-dimensional approximate message passing methodology.
We demonstrate the effectiveness of this approach across five numerical scenarios.
arXiv Detail & Related papers (2023-11-14T17:31:50Z) - Nonparametric Identifiability of Causal Representations from Unknown
Interventions [63.1354734978244]
We study causal representation learning, the task of inferring latent causal variables and their causal relations from mixtures of the variables.
Our goal is to identify both the ground truth latents and their causal graph up to a set of ambiguities which we show to be irresolvable from interventional data.
arXiv Detail & Related papers (2023-06-01T10:51:58Z) - Inferring Causal Effects Under Heterogeneous Peer Influence [12.533920403498453]
Causal inference in networks should account for interference, which occurs when a unit's outcome is influenced by treatments or outcomes of peers.
We propose a structural causal model for networks that can capture different possible assumptions about network structure, interference conditions, and causal dependence.
We find potential heterogeneous contexts using the causal model and propose a novel graph neural network-based estimator to estimate individual direct causal effects.
arXiv Detail & Related papers (2023-05-27T13:57:26Z) - 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) - Learning Individual Treatment Effects under Heterogeneous Interference
in Networks [34.16062968227468]
Estimates of individual treatment effects from networked observational data are attracting increasing attention.
One major challenge in network scenarios is the violation of the stable unit treatment value assumption.
We propose a novel Dual Weighting Regression (DWR) algorithm by simultaneously learning attention weights.
arXiv Detail & Related papers (2022-10-25T15:00:05Z) - Information Interaction Profile of Choice Adoption [2.9972063833424216]
We introduce an efficient method to infer the entities interaction network and its evolution according to the temporal distance separating interacting entities.
The interaction profile allows characterizing the mechanisms of the interaction processes.
We show that the effect of a combination of exposures on a user is more than the sum of each exposure's independent effect--there is an interaction.
arXiv Detail & Related papers (2021-04-28T10:42:25Z) - 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) - Interference and Generalization in Temporal Difference Learning [86.31598155056035]
We study the link between generalization and interference in temporal-difference (TD) learning.
We find that TD easily leads to low-interference, under-generalizing parameters, while the effect seems reversed in supervised learning.
arXiv Detail & Related papers (2020-03-13T15:49:58Z) - Almost-Matching-Exactly for Treatment Effect Estimation under Network
Interference [73.23326654892963]
We propose a matching method that recovers direct treatment effects from randomized experiments where units are connected in an observed network.
Our method matches units almost exactly on counts of unique subgraphs within their neighborhood graphs.
arXiv Detail & Related papers (2020-03-02T15:21:20Z) - 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) - Nonparametric inference for interventional effects with multiple
mediators [0.0]
We provide theory that allows for more flexible, possibly machine learning-based, estimation techniques.
We demonstrate multiple robustness properties of the proposed estimators.
Our work thus provides a means of leveraging modern statistical learning techniques in estimation of interventional mediation effects.
arXiv Detail & Related papers (2020-01-16T19:05:00Z)
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.