Heyang Gong - Profile on Academia.edu (original) (raw)

Papers by Heyang Gong

arXiv (Cornell University), Sep 16, 2023

In the realm of causal inference, Potential Outcomes (PO) and Structural Causal Models (SCM) are ... more In the realm of causal inference, Potential Outcomes (PO) and Structural Causal Models (SCM) are recognized as the principal frameworks. However, when it comes to addressing Layer 3 valuations-counterfactual queries deeply entwined with individuallevel semantics-both frameworks encounter limitations due to the degenerative issues brought forth by the consistency rule. This paper advocates for the Distribution-consistency Structural Causal Models (DiscoSCM) framework as a pioneering approach to counterfactual inference, skillfully integrating the strengths of both PO and SCM. The DiscoSCM framework distinctively incorporates a unit selection variable 𝑈 and embraces the concept of uncontrollable exogenous noise realization. Through personalized incentive scenarios, we demonstrate the inadequacies of PO and SCM frameworks in representing the probability of a user being a complier (a Layer 3 event) without degeneration, an issue adeptly resolved by adopting the assumption of independent counterfactual noises within DiscoSCM. This innovative assumption broadens the foundational counterfactual theory, facilitating the extension of numerous theoretical results regarding the probability of causation to an individual granularity level and leading to a comprehensive set of theories on heterogeneous counterfactual bounds. Ultimately, our paper posits that if one acknowledges and wishes to leverage the ubiquitous heterogeneity, understanding causality as invariance across heterogeneous units, then DiscoSCM stands as a significant advancement in the methodology of counterfactual inference.

arXiv (Cornell University), Jan 29, 2024

In the field of causal modeling, potential outcomes (PO) and structural causal models (SCMs) stan... more In the field of causal modeling, potential outcomes (PO) and structural causal models (SCMs) stand as the predominant frameworks. However, these frameworks face notable challenges in practically modeling counterfactuals, formalized as parameters of the joint distribution of potential outcomes. Counterfactual reasoning holds paramount importance in contemporary decision-making processes, especially in scenarios that demand personalized incentives based on the joint values of (Y (0), Y (1)). This paper begins with an investigation of the PO and SCM frameworks for modeling counterfactuals. Through the analysis, we identify an inherent model capacity limitation, termed as the "degenerative counterfactual problem", emerging from the consistency rule that is the cornerstone of both frameworks. To address this limitation, we introduce a novel distribution-consistency assumption, and in alignment with it, we propose the Distribution-consistency Structural Causal Models (DiscoSCMs) offering enhanced capabilities to model counterfactuals. Furthermore, we provide a comprehensive set of theoretical results about the "Ladder of Causation" within the DiscoSCM framework. To concretely reveal the enhanced model capacity, we introduce a new identifiable causal parameter, the probability of consistency, which holds practical significance within DiscoSCM alone, showcased with a personalized incentive example. We hope it opens new avenues for future research of counterfactual modeling, ultimately enhancing our understanding of causality and its real-world applications.

arXiv (Cornell University), Jun 6, 2021

Path-specific effects in mediation analysis provide a useful tool for fairness analysis, which is... more Path-specific effects in mediation analysis provide a useful tool for fairness analysis, which is mostly based on nested counterfactuals. However, the dictum "no causation without manipulation" implies that path-specific effects might be induced by certain interventions. This paper proposes a new path intervention inspired by information accounts of causality, and develops the corresponding intervention diagrams and π-formula. Compared with the interventionist approach of Robins et al. (2020) based on nested counterfactuals, our proposed path intervention method explicitly describes the manipulation in structural causal model with a simple information transferring interpretation, and does not require the non-existence of recanting witness to identify path-specific effects. Hence, it could serve useful communications and theoretical focuses for mediation analysis.

Connecting Data to Mechanisms with Meta Structural Causal Model

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)

The potential outcome framework and structural causal model are two main frameworks for causal mo... more The potential outcome framework and structural causal model are two main frameworks for causal modeling, and there are efforts to combine the merits of each framework, such as the single world intervention graph (SWIG) and its potential outcome calculus. In this paper, we propose the info intervention inspired by understanding the causality as information transfer, and provide the corresponding causal calculus. On one hand, we explain the connection between info calculus and do calculus. On the other hand, we show that the info calculus is as convenient as the SWIG to check the conditional independence, and most importantly, it owns an operator σ(·) for formalizing causal queries.

ArXiv, 2022

Offering incentives (e.g., coupons at Amazon, discounts at Uber and video bonuses at Tiktok) to u... more Offering incentives (e.g., coupons at Amazon, discounts at Uber and video bonuses at Tiktok) to user is a common strategy used by online platforms to increase user engagement and platform revenue. Despite its proven effectiveness, thesemarketing incentives incur an inevitable cost and might result in a low ROI (Return on Investment) if not used properly. On the other hand, different users respond differently to these incentives, for instance, some users never buy certain products without coupons, while others do anyway. Thus, how to select the right amount of incentives (i.e. treatment) to each user under budget constraints is an important research problem with great practical implications. In this paper, we call such problem as a budget-constrained treatment selection (BTS) problem. The challenge is how to efficiently solve BTS problem on a Large-Scale dataset and achieve improved results over the existing techniques. We propose a novel tree-based treatment selection technique unde...

Path-specific effects in mediation analysis provide a useful tool for fairness analysis, which is... more Path-specific effects in mediation analysis provide a useful tool for fairness analysis, which is mostly based on nested counterfactuals. However, the dictum “no causation without manipulation” implies that path-specific effects might be induced by certain interventions. This paper proposes a new path intervention inspired by information accounts of causality, and develops the corresponding intervention diagrams and π-formula. Compared with the interventionist approach of Robins et al.(2020) based on nested counterfactuals, our proposed path intervention method explicitly describes the manipulation in structural causal model with a simple information transferring interpretation, and does not require the non-existence of recanting witness to identify path-specific effects. Hence, it could serve useful communications and theoretical focus for mediation analysis.

arXiv (Cornell University), Sep 16, 2023

In the realm of causal inference, Potential Outcomes (PO) and Structural Causal Models (SCM) are ... more In the realm of causal inference, Potential Outcomes (PO) and Structural Causal Models (SCM) are recognized as the principal frameworks. However, when it comes to addressing Layer 3 valuations-counterfactual queries deeply entwined with individuallevel semantics-both frameworks encounter limitations due to the degenerative issues brought forth by the consistency rule. This paper advocates for the Distribution-consistency Structural Causal Models (DiscoSCM) framework as a pioneering approach to counterfactual inference, skillfully integrating the strengths of both PO and SCM. The DiscoSCM framework distinctively incorporates a unit selection variable 𝑈 and embraces the concept of uncontrollable exogenous noise realization. Through personalized incentive scenarios, we demonstrate the inadequacies of PO and SCM frameworks in representing the probability of a user being a complier (a Layer 3 event) without degeneration, an issue adeptly resolved by adopting the assumption of independent counterfactual noises within DiscoSCM. This innovative assumption broadens the foundational counterfactual theory, facilitating the extension of numerous theoretical results regarding the probability of causation to an individual granularity level and leading to a comprehensive set of theories on heterogeneous counterfactual bounds. Ultimately, our paper posits that if one acknowledges and wishes to leverage the ubiquitous heterogeneity, understanding causality as invariance across heterogeneous units, then DiscoSCM stands as a significant advancement in the methodology of counterfactual inference.

arXiv (Cornell University), Jan 29, 2024

In the field of causal modeling, potential outcomes (PO) and structural causal models (SCMs) stan... more In the field of causal modeling, potential outcomes (PO) and structural causal models (SCMs) stand as the predominant frameworks. However, these frameworks face notable challenges in practically modeling counterfactuals, formalized as parameters of the joint distribution of potential outcomes. Counterfactual reasoning holds paramount importance in contemporary decision-making processes, especially in scenarios that demand personalized incentives based on the joint values of (Y (0), Y (1)). This paper begins with an investigation of the PO and SCM frameworks for modeling counterfactuals. Through the analysis, we identify an inherent model capacity limitation, termed as the "degenerative counterfactual problem", emerging from the consistency rule that is the cornerstone of both frameworks. To address this limitation, we introduce a novel distribution-consistency assumption, and in alignment with it, we propose the Distribution-consistency Structural Causal Models (DiscoSCMs) offering enhanced capabilities to model counterfactuals. Furthermore, we provide a comprehensive set of theoretical results about the "Ladder of Causation" within the DiscoSCM framework. To concretely reveal the enhanced model capacity, we introduce a new identifiable causal parameter, the probability of consistency, which holds practical significance within DiscoSCM alone, showcased with a personalized incentive example. We hope it opens new avenues for future research of counterfactual modeling, ultimately enhancing our understanding of causality and its real-world applications.

arXiv (Cornell University), Jun 6, 2021

Path-specific effects in mediation analysis provide a useful tool for fairness analysis, which is... more Path-specific effects in mediation analysis provide a useful tool for fairness analysis, which is mostly based on nested counterfactuals. However, the dictum "no causation without manipulation" implies that path-specific effects might be induced by certain interventions. This paper proposes a new path intervention inspired by information accounts of causality, and develops the corresponding intervention diagrams and π-formula. Compared with the interventionist approach of Robins et al. (2020) based on nested counterfactuals, our proposed path intervention method explicitly describes the manipulation in structural causal model with a simple information transferring interpretation, and does not require the non-existence of recanting witness to identify path-specific effects. Hence, it could serve useful communications and theoretical focuses for mediation analysis.

Connecting Data to Mechanisms with Meta Structural Causal Model

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)

The potential outcome framework and structural causal model are two main frameworks for causal mo... more The potential outcome framework and structural causal model are two main frameworks for causal modeling, and there are efforts to combine the merits of each framework, such as the single world intervention graph (SWIG) and its potential outcome calculus. In this paper, we propose the info intervention inspired by understanding the causality as information transfer, and provide the corresponding causal calculus. On one hand, we explain the connection between info calculus and do calculus. On the other hand, we show that the info calculus is as convenient as the SWIG to check the conditional independence, and most importantly, it owns an operator σ(·) for formalizing causal queries.

ArXiv, 2022

Offering incentives (e.g., coupons at Amazon, discounts at Uber and video bonuses at Tiktok) to u... more Offering incentives (e.g., coupons at Amazon, discounts at Uber and video bonuses at Tiktok) to user is a common strategy used by online platforms to increase user engagement and platform revenue. Despite its proven effectiveness, thesemarketing incentives incur an inevitable cost and might result in a low ROI (Return on Investment) if not used properly. On the other hand, different users respond differently to these incentives, for instance, some users never buy certain products without coupons, while others do anyway. Thus, how to select the right amount of incentives (i.e. treatment) to each user under budget constraints is an important research problem with great practical implications. In this paper, we call such problem as a budget-constrained treatment selection (BTS) problem. The challenge is how to efficiently solve BTS problem on a Large-Scale dataset and achieve improved results over the existing techniques. We propose a novel tree-based treatment selection technique unde...

Path-specific effects in mediation analysis provide a useful tool for fairness analysis, which is... more Path-specific effects in mediation analysis provide a useful tool for fairness analysis, which is mostly based on nested counterfactuals. However, the dictum “no causation without manipulation” implies that path-specific effects might be induced by certain interventions. This paper proposes a new path intervention inspired by information accounts of causality, and develops the corresponding intervention diagrams and π-formula. Compared with the interventionist approach of Robins et al.(2020) based on nested counterfactuals, our proposed path intervention method explicitly describes the manipulation in structural causal model with a simple information transferring interpretation, and does not require the non-existence of recanting witness to identify path-specific effects. Hence, it could serve useful communications and theoretical focus for mediation analysis.