An improved neural network model for treatment effect estimation

• URL: http://arxiv.org/abs/2205.11106v1
• Date: Mon, 23 May 2022 07:56:06 GMT
• Title: An improved neural network model for treatment effect estimation
• Authors: Niki Kiriakidou and Christos Diou
• Abstract summary: 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.
• Score: 3.1372269816123994
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Nowadays, in many scientific and industrial fields there is an increasing need for estimating treatment effects and answering causal questions. The key for addressing these problems is the wealth of observational data and the processes for leveraging this data. In this work, we propose a new model for predicting the potential outcomes and the propensity score, which is based on a neural network architecture. The proposed model exploits the covariates as well as the outcomes of neighboring instances in training data. Numerical experiments illustrate that the proposed model reports better treatment effect estimation performance compared to state-of-the-art models.

Related papers

• Estimating Causal Effects from Learned Causal Networks [56.14597641617531]
  We propose an alternative paradigm for answering causal-effect queries over discrete observable variables. We learn the causal Bayesian network and its confounding latent variables directly from the observational data. We show that this emphmodel completion learning approach can be more effective than estimand approaches.
  arXiv  Detail & Related papers  (2024-08-26T08:39:09Z)
• 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)
• C-XGBoost: A tree boosting model for causal effect estimation [8.246161706153805]
  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.
  arXiv  Detail & Related papers  (2024-03-31T17:43:37Z)
• Uplift Modeling based on Graph Neural Network Combined with Causal Knowledge [9.005051998738134]
  We propose a framework based on graph neural networks that combine causal knowledge with an estimate of uplift value. Our findings demonstrate that this method works effectively for predicting uplift values, with small errors in typical simulated data.
  arXiv  Detail & Related papers  (2023-11-14T07:21:00Z)
• Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study [71.84852429039881]
  Investigation focuses on the models' ability to handle a range of perturbations, such as sensor faults and noise. We test the generalization and transfer learning capabilities of these models by exposing them to out-of-distribution (OOD) samples.
  arXiv  Detail & Related papers  (2023-06-13T12:43:59Z)
• Integrating Nearest Neighbors with Neural Network Models for Treatment Effect Estimation [3.1372269816123994]
  We propose Nearest Neighboring Information for Causal Inference (NNCI) for integrating valuable nearest neighboring information on neural network-based models for estimating treatment effects. NNCI is applied to some of the most well established neural network-based models for treatment effect estimation with the use of observational data.
  arXiv  Detail & Related papers  (2023-05-11T13:24:10Z)
• 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)
• Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability [82.29775890542967]
  Estimating personalized effects of treatments is a complex, yet pervasive problem. Recent developments in the machine learning literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools. We use post-hoc feature importance methods to identify features that influence the model's predictions.
  arXiv  Detail & Related papers  (2022-06-16T17:59:05Z)
• Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future [73.03458424369657]
  In real-time forecasting in public health, data collection is a non-trivial and demanding task. 'Backfill' phenomenon and its effect on model performance has been barely studied in the prior literature. We formulate a novel problem and neural framework Back2Future that aims to refine a given model's predictions in real-time.
  arXiv  Detail & Related papers  (2021-06-08T14:48:20Z)
• Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning [109.74041512359476]
  We study a number of design decisions for the predictive model in visual MBRL algorithms. We find that a range of design decisions that are often considered crucial, such as the use of latent spaces, have little effect on task performance. We show how this phenomenon is related to exploration and how some of the lower-scoring models on standard benchmarks will perform the same as the best-performing models when trained on the same training data.
  arXiv  Detail & Related papers  (2020-12-08T18:03:21Z)
• DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and their Interactions [2.30238915794052]
  We propose a deep learning model to forecast the range of increase in COVID-19 infected cases in future days. Using data collected from various sources, we estimate the range of increase in infected cases seven days into the future for all U.S. counties.
  arXiv  Detail & Related papers  (2020-07-31T23:37:38Z)

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.