1 code implementation • 6 May 2024 • Weilin Chen, Ruichu Cai, Zeqin Yang, Jie Qiao, Yuguang Yan, Zijian Li, Zhifeng Hao
Based on the condition, we devise an end-to-end causal effect estimator by transforming the identified theoretical condition into a targeted loss.
no code implementations • 25 Mar 2024 • Jie Qiao, Yu Xiang, Zhengming Chen, Ruichu Cai, Zhifeng Hao
Fortunately, in this work, we found that the causal order from $X$ to its child $Y$ is identifiable if $X$ is a root vertex and has at least two directed paths to $Y$, or the ancestor of $X$ with the most directed path to $X$ has a directed path to $Y$ without passing $X$.
no code implementations • 24 Feb 2024 • Zijian Li, Ruichu Cai, Haiqin Huang, Sili Zhang, Yuguang Yan, Zhifeng Hao, Zhenghua Dong
Existing model-based interactive recommendation systems are trained by querying a world model to capture the user preference, but learning the world model from historical logged data will easily suffer from bias issues such as popularity bias and sampling bias.
no code implementations • 20 Feb 2024 • Zijian Li, Ruichu Cai, Zhenhui Yang, Haiqin Huang, Guangyi Chen, Yifan Shen, Zhengming Chen, Xiangchen Song, Zhifeng Hao, Kun Zhang
To solve this problem, we propose to learn IDentifiable latEnt stAtes (IDEA) to detect when the distribution shifts occur.
no code implementations • 14 Feb 2024 • Xuexin Chen, Ruichu Cai, Kaitao Zheng, Zhifan Jiang, Zhengting Huang, Zhifeng Hao, Zijian Li
Under mild conditions, we show that the invariant subgraph can be extracted by minimizing an upper bound, which is built on the theoretical advance of probability of necessity and sufficiency.
no code implementations • 13 Feb 2024 • Xuexin Chen, Ruichu Cai, Zhengting Huang, Yuxuan Zhu, Julien Horwood, Zhifeng Hao, Zijian Li, Jose Miguel Hernandez-Lobato
We investigate the problem of explainability in machine learning. To address this problem, Feature Attribution Methods (FAMs) measure the contribution of each feature through a perturbation test, where the difference in prediction is compared under different perturbations. However, such perturbation tests may not accurately distinguish the contributions of different features, when their change in prediction is the same after perturbation. In order to enhance the ability of FAMs to distinguish different features' contributions in this challenging setting, we propose to utilize the probability (PNS) that perturbing a feature is a necessary and sufficient cause for the prediction to change as a measure of feature importance. Our approach, Feature Attribution with Necessity and Sufficiency (FANS), computes the PNS via a perturbation test involving two stages (factual and interventional). In practice, to generate counterfactual samples, we use a resampling-based approach on the observed samples to approximate the required conditional distribution. Finally, we combine FANS and gradient-based optimization to extract the subset with the largest PNS. We demonstrate that FANS outperforms existing feature attribution methods on six benchmarks.
no code implementations • 7 Feb 2024 • Ruichu Cai, Siyang Huang, Jie Qiao, Wei Chen, Yan Zeng, Keli Zhang, Fuchun Sun, Yang Yu, Zhifeng Hao
As a key component to intuitive cognition and reasoning solutions in human intelligence, causal knowledge provides great potential for reinforcement learning (RL) agents' interpretability towards decision-making by helping reduce the searching space.
1 code implementation • 5 Jan 2024 • Zichen Wen, Yawen Ling, Yazhou Ren, Tianyi Wu, Jianpeng Chen, Xiaorong Pu, Zhifeng Hao, Lifang He
Then we design an adaptive hybrid graph filter that is related to the homophily degree, which learns the node embedding based on the graph joint aggregation matrix.
no code implementations • 21 Dec 2023 • Ruichu Cai, Yuxuan Zhu, Jie Qiao, Zefeng Liang, Furui Liu, Zhifeng Hao
By considering the underappreciated causal generating process, first, we pinpoint the source of the vulnerability of DNNs via the lens of causality, then give theoretical results to answer \emph{where to attack}.
no code implementations • 19 Dec 2023 • Jie Qiao, Zhengming Chen, Jianhua Yu, Ruichu Cai, Zhifeng Hao
With this observation, we aim to investigate the identification problem of learning causal structure from missing data under an additive noise model with different missingness mechanisms, where the `no self-masking missingness' assumption can be eliminated appropriately.
no code implementations • 19 Dec 2023 • Wei Chen, Zhiyi Huang, Ruichu Cai, Zhifeng Hao, Kun Zhang
Despite the emergence of numerous methods aimed at addressing this challenge, they are not fully identified to the structure that two observed variables are influenced by one latent variable and there might be a directed edge in between.
1 code implementation • 18 Dec 2023 • Kun Peng, Lei Jiang, Hao Peng, Rui Liu, Zhengtao Yu, Jiaqian Ren, Zhifeng Hao, Philip S. Yu
Aspect Sentiment Triplet Extraction (ASTE) is an emerging task to extract a given sentence's triplets, which consist of aspects, opinions, and sentiments.
1 code implementation • 8 Nov 2023 • Zijian Li, Zunhong Xu, Ruichu Cai, Zhenhui Yang, Yuguang Yan, Zhifeng Hao, Guangyi Chen, Kun Zhang
Specifically, we first formulate the data generation process from the atom level to the molecular level, where the latent space is split into SI substructures, SR substructures, and SR atom variables.
1 code implementation • NeurIPS 2023 • Zijian Li, Ruichu Cai, Guangyi Chen, Boyang Sun, Zhifeng Hao, Kun Zhang
To mitigate the need for these strict assumptions, we propose a subspace identification theory that guarantees the disentanglement of domain-invariant and domain-specific variables under less restrictive constraints regarding domain numbers and transformation properties, thereby facilitating domain adaptation by minimizing the impact of domain shifts on invariant variables.
no code implementations • 24 Sep 2023 • Xinyue Chen, Jie Xu, Yazhou Ren, Xiaorong Pu, Ce Zhu, Xiaofeng Zhu, Zhifeng Hao, Lifang He
Second, the storage and usage of data from multiple clients in a distributed environment can lead to incompleteness of multi-view data.
no code implementations • 8 Aug 2023 • Ruichu Cai, Zeqin Yang, Weilin Chen, Yuguang Yan, Zhifeng Hao
To fill this gap, we derive a generalization bound for causal effect estimation in network scenarios by exploiting 1) the reweighting schema based on joint propensity score and 2) the representation learning schema based on Integral Probability Metric (IPM).
no code implementations • 25 Jun 2023 • Yuequn Liu, Ruichu Cai, Wei Chen, Jie Qiao, Yuguang Yan, Zijian Li, Keli Zhang, Zhifeng Hao
assumption is often violated due to the inherent dependencies among the event sequences.
no code implementations • 31 May 2023 • Ruichu Cai, Zhiyi Huang, Wei Chen, Zhifeng Hao, Kun Zhang
In light of the power of the closed-form solution to OICA corresponding to the One-Latent-Component structure, we formulate a way to estimate the mixing matrix using the higher-order cumulants, and further propose the testable One-Latent-Component condition to identify the latent variables and determine causal orders.
1 code implementation • 10 May 2023 • Jie Qiao, Ruichu Cai, Siyu Wu, Yu Xiang, Keli Zhang, Zhifeng Hao
Learning causal structure among event types from discrete-time event sequences is a particularly important but challenging task.
no code implementations • 10 Feb 2023 • Yan Zeng, Ruichu Cai, Fuchun Sun, Libo Huang, Zhifeng Hao
While Reinforcement Learning (RL) achieves tremendous success in sequential decision-making problems of many domains, it still faces key challenges of data inefficiency and the lack of interpretability.
no code implementations • 14 Dec 2022 • Ruichu Cai, Yuxuan Zhu, Xuexin Chen, Yuan Fang, Min Wu, Jie Qiao, Zhifeng Hao
To address the non-identifiability of PNS, we resort to a lower bound of PNS that can be optimized via counterfactual estimation, and propose a framework of Necessary and Sufficient Explanation for GNN (NSEG) via optimizing that lower bound.
no code implementations • 13 Oct 2022 • Jianpeng Chen, Yawen Ling, Jie Xu, Yazhou Ren, Shudong Huang, Xiaorong Pu, Zhifeng Hao, Philip S. Yu, Lifang He
The critical point of MGC is to better utilize the view-specific and view-common information in features and graphs of multiple views.
1 code implementation • 30 Dec 2021 • Xuexin Chen, Ruichu Cai, Yuan Fang, Min Wu, Zijian Li, Zhifeng Hao
However, standard GNNs in the neighborhood aggregation paradigm suffer from limited discriminative power in distinguishing \emph{high-order} graph structures as opposed to \emph{low-order} structures.
1 code implementation • 18 Nov 2021 • Wei Chen, Yunjin Wu, Ruichu Cai, Yueguo Chen, Zhifeng Hao
This method simultaneously integrates the following two tasks: 1) clustering samples of the subjects with the same causal mechanism into different groups; 2) learning causal structures from the samples within the group.
1 code implementation • 5 Nov 2021 • Zijian Li, Ruichu Cai, Tom Z. J Fu, Zhifeng Hao, Kun Zhang
In order to address these challenges, we analyze variational conditional dependencies in time-series data and find that the causal structures are usually stable among domains, and further raise the causal conditional shift assumption.
1 code implementation • NeurIPS 2021 • Ruichu Cai, Jinjie Yuan, Boyan Xu, Zhifeng Hao
The key lies in the generalizability of (i) the encoding method to model the question and the database schema and (ii) the question-schema linking method to learn the mapping between words in the question and tables/columns in the database schema.
Ranked #4 on Text-To-SQL on spider (Exact Match Accuracy (Test) metric)
1 code implementation • 5 Jun 2021 • Weilin Chen, Jie Qiao, Ruichu Cai, Zhifeng Hao
Causal discovery from observational data is an important but challenging task in many scientific fields.
no code implementations • 23 May 2021 • Ruichu Cai, Siyu Wu, Jie Qiao, Zhifeng Hao, Keli Zhang, Xi Zhang
We further propose a causal structure learning method on THP in a likelihood framework.
no code implementations • 26 Mar 2021 • Wei Chen, Kun Zhang, Ruichu Cai, Biwei Huang, Joseph Ramsey, Zhifeng Hao, Clark Glymour
The first step of our method uses the FCI procedure, which allows confounders and is able to produce asymptotically correct results.
1 code implementation • 22 Dec 2020 • Zhifeng Hao, Di Lv, Zijian Li, Ruichu Cai, Wen Wen, Boyan Xu
In the proposed framework, the domain-specific information is integrated with the domain-specific latent variables by using a domain predictor.
1 code implementation • 22 Dec 2020 • Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, Zhifeng Hao
Different from previous efforts on the entangled feature space, we aim to extract the domain invariant semantic information in the latent disentangled semantic representation (DSR) of the data.
no code implementations • NeurIPS 2020 • Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang
Despite its success in certain domains, most existing methods focus on causal relations between observed variables, while in many scenarios the observed ones may not be the underlying causal variables (e. g., image pixels), but are generated by latent causal variables or confounders that are causally related.
no code implementations • 19 Sep 2020 • Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, Zhifeng Hao
In this paper, we propose Multi-Domain Linear Non-Gaussian Acyclic Models for Latent Factors (MD-LiNA), where the causal structure among latent factors of interest is shared for all domains, and we provide its identification results.
no code implementations • NeurIPS 2019 • Ruichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang
In this paper, by properly leveraging the non-Gaussianity of the data, we propose to estimate the structure over latent variables with the so-called Triad constraints: we design a form of "pseudo-residual" from three variables, and show that when causal relations are linear and noise terms are non-Gaussian, the causal direction between the latent variables for the three observed variables is identifiable by checking a certain kind of independence relationship.
2 code implementations • 23 May 2019 • Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao
In this work, we propose a cascade nonlinear additive noise model to represent such causal influences--each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect.
no code implementations • NeurIPS 2018 • Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao
In this paper we make an attempt to find a way to solve this problem by assuming a two-stage causal process: the first stage maps the cause to a hidden variable of a lower cardinality, and the second stage generates the effect from the hidden representation.
no code implementations • 5 Jul 2017 • Ruichu Cai, Zhenjie Zhang, Zhifeng Hao
We theoretically prove that SADA always reduces the scales of problems without sacrifice on accuracy, under the condition of local causal sparsity and reliable conditional independence tests.
1 code implementation • 13 Feb 2016 • Ganzhao Yuan, Yin Yang, Zhenjie Zhang, Zhifeng Hao
This paper points out that under ($\epsilon$, $\delta$)-differential privacy, the optimal solution of the above constrained optimization problem in search of a suitable strategy can be found, rather surprisingly, by solving a simple and elegant convex optimization program.
no code implementations • 31 Jul 2014 • Lifang He, Xiangnan Kong, Philip S. Yu, Ann B. Ragin, Zhifeng Hao, Xiaowei Yang
The dual-tensorial mapping function can map each tensor instance in the input space to another tensor in the feature space while preserving the tensorial structure.