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Estimating Individualized Causal Effect with Confounded Instruments
Knowledge Discovery and Data Mining, 2022Learning individualized causal effect (ICE) plays a vital role in various fields of big data analysis, ranging from fine-grained policy evaluation to personalized treatment development.
Haotian Wang +7 more
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Quantifying the Causal Effect of Individual Mobility on Health Status in Urban Space
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies, 2021How does individual mobility in the urban environment impact their health status? Previous works have explored the correlation between human mobility behaviour and individual health, yet the study on the underlying causal effect is woefully inadequate ...
Yun-Ke Zhang +3 more
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Do-PFN: In-Context Learning for Causal Effect Estimation
Neural Information Processing SystemsEstimation of causal effects is critical to a range of scientific disciplines. Existing methods for this task either require interventional data, knowledge about the ground truth causal graph, or rely on assumptions such as unconfoundedness, restricting ...
Jake Robertson +5 more
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Identifying Causal Effect Inference Failure with Uncertainty-Aware Models
Neural Information Processing Systems, 2020Recommending the best course of action for an individual is a major application of individual-level causal effect estimation. This application is often needed in safety-critical domains such as healthcare, where estimating and communicating uncertainty ...
A. Jesson +3 more
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CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
Neural Information Processing SystemsCausal effect estimation from observational data is fundamental across various applications. However, selecting an appropriate estimator from dozens of specialized methods demands substantial manual effort and domain expertise.
Vahid Balazadeh Meresht +6 more
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Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning
International Conference on Machine LearningCausal effect estimation under networked interference is an important but challenging problem. Available parametric methods are limited in their model space, while previous semiparametric methods, e.g., leveraging neural networks to fit only one single ...
Weilin Chen +6 more
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End-To-End Causal Effect Estimation from Unstructured Natural Language Data
Neural Information Processing SystemsKnowing the effect of an intervention is critical for human decision-making, but current approaches for causal effect estimation rely on manual data collection and structuring, regardless of the causal assumptions.
Nikita Dhawan +4 more
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, 2020
Omitted variables create endogeneity and thus bias the estimation of the causal effect of measured variables on outcomes. Such measured variables are ubiquitous and include perceptions, attitudes, emotions, behaviors, and choices.
G. Sajons
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Omitted variables create endogeneity and thus bias the estimation of the causal effect of measured variables on outcomes. Such measured variables are ubiquitous and include perceptions, attitudes, emotions, behaviors, and choices.
G. Sajons
semanticscholar +1 more source
Invariant Graph Learning for Causal Effect Estimation
The Web ConferenceCausal effect estimation from networked observational data encounters notable challenges, primarily hidden confounders arising from network structure, or spillover effects that influence unit's outcomes based on neighboring treatment assignments ...
Yongduo Sui +8 more
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Education and adult health: Is there a causal effect?
Social Science & Medicine (1967), 2020Many studies find a strong positive correlation between education and adult health. A subtler question is whether this correlation can be interpreted as a causal relationship.
P. Albarrán +2 more
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