C-XGBoost: A tree boosting model for causal effect estimation
- URL: http://arxiv.org/abs/2404.00751v1
- Date: Sun, 31 Mar 2024 17:43:37 GMT
- Title: C-XGBoost: A tree boosting model for causal effect estimation
- Authors: Niki Kiriakidou, Ioannis E. Livieris, Christos Diou,
- Abstract summary: Causal effect estimation aims at estimating the Average Treatment Effect as well as the Conditional Average Treatment Effect of a treatment to an outcome from the available data.
We propose a new causal inference model, named C-XGBoost, for the prediction of potential outcomes.
- Score: 8.246161706153805
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Causal effect estimation aims at estimating the Average Treatment Effect as well as the Conditional Average Treatment Effect of a treatment to an outcome from the available data. This knowledge is important in many safety-critical domains, where it often needs to be extracted from observational data. In this work, we propose a new causal inference model, named C-XGBoost, for the prediction of potential outcomes. The motivation of our approach is to exploit the superiority of tree-based models for handling tabular data together with the notable property of causal inference neural network-based models to learn representations that are useful for estimating the outcome for both the treatment and non-treatment cases. The proposed model also inherits the considerable advantages of XGBoost model such as efficiently handling features with missing values requiring minimum preprocessing effort, as well as it is equipped with regularization techniques to avoid overfitting/bias. Furthermore, we propose a new loss function for efficiently training the proposed causal inference model. The experimental analysis, which is based on the performance profiles of Dolan and Mor{\'e} as well as on post-hoc and non-parametric statistical tests, provide strong evidence about the effectiveness of the proposed approach.
Related papers
- Causal Fine-Tuning and Effect Calibration of Non-Causal Predictive Models [1.3124513975412255]
This paper proposes techniques to enhance the performance of non-causal models for causal inference using data from randomized experiments.
In domains like advertising, customer retention, and precision medicine, non-causal models that predict outcomes under no intervention are often used to score individuals and rank them according to the expected effectiveness of an intervention.
arXiv Detail & Related papers (2024-06-13T20:18:16Z) - Neural Networks with Causal Graph Constraints: A New Approach for Treatment Effects Estimation [0.951494089949975]
We present a new model, NN-CGC, that considers additional information from the causal graph.
We show that our method is robust to imperfect causal graphs and that using partial causal information is preferable to ignoring it.
arXiv Detail & Related papers (2024-04-18T14:57:17Z) - Efficient adjustment for complex covariates: Gaining efficiency with
DOPE [56.537164957672715]
We propose a framework that accommodates adjustment for any subset of information expressed by the covariates.
Based on our theoretical results, we propose the Debiased Outcome-adapted Propensity Estorimator (DOPE) for efficient estimation of the average treatment effect (ATE)
Our results show that the DOPE provides an efficient and robust methodology for ATE estimation in various observational settings.
arXiv Detail & Related papers (2024-02-20T13:02:51Z) - A PAC-Bayesian Perspective on the Interpolating Information Criterion [54.548058449535155]
We show how a PAC-Bayes bound is obtained for a general class of models, characterizing factors which influence performance in the interpolating regime.
We quantify how the test error for overparameterized models achieving effectively zero training error depends on the quality of the implicit regularization imposed by e.g. the combination of model, parameter-initialization scheme.
arXiv Detail & Related papers (2023-11-13T01:48:08Z) - Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
We propose a framework for efficient and effective counterfactual inference implemented with neural networks.
The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data.
Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
arXiv Detail & Related papers (2023-06-09T08:30:51Z) - B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under
Hidden Confounding [51.74479522965712]
We propose a meta-learner called the B-Learner, which can efficiently learn sharp bounds on the CATE function under limits on hidden confounding.
We prove its estimates are valid, sharp, efficient, and have a quasi-oracle property with respect to the constituent estimators under more general conditions than existing methods.
arXiv Detail & Related papers (2023-04-20T18:07:19Z) - NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation [37.361149306896024]
Causal effect estimation from observational data is a central problem in causal inference.
We propose an adaptive method called Neurosymbolic Causal Effect Estimator (NESTER)
Our comprehensive empirical results show that NESTER performs better than state-of-the-art methods on benchmark datasets.
arXiv Detail & Related papers (2022-11-08T16:48:46Z) - An evaluation framework for comparing causal inference models [3.1372269816123994]
We use the proposed evaluation methodology to compare several state-of-the-art causal effect estimation models.
The main motivation behind this approach is the elimination of the influence of a small number of instances or simulation on the benchmarking process.
arXiv Detail & Related papers (2022-08-31T21:04:20Z) - Measuring Causal Effects of Data Statistics on Language Model's
`Factual' Predictions [59.284907093349425]
Large amounts of training data are one of the major reasons for the high performance of state-of-the-art NLP models.
We provide a language for describing how training data influences predictions, through a causal framework.
Our framework bypasses the need to retrain expensive models and allows us to estimate causal effects based on observational data alone.
arXiv Detail & Related papers (2022-07-28T17:36:24Z) - An improved neural network model for treatment effect estimation [3.1372269816123994]
We propose a new model for predicting the potential outcomes and the propensity score, which is based on a neural network architecture.
Numerical experiments illustrate that the proposed model reports better treatment effect estimation performance compared to state-of-the-art models.
arXiv Detail & Related papers (2022-05-23T07:56:06Z) - Decomposed Adversarial Learned Inference [118.27187231452852]
We propose a novel approach, Decomposed Adversarial Learned Inference (DALI)
DALI explicitly matches prior and conditional distributions in both data and code spaces.
We validate the effectiveness of DALI on the MNIST, CIFAR-10, and CelebA datasets.
arXiv Detail & Related papers (2020-04-21T20:00:35Z)
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.