paper-with-me

홈 › Papers

Time-Varying Bidirectional Causal Relationships Between Transaction Fees and Economic Activity of Subsystems Utilizing the Ethereum Blockchain Network

2025-01-09 · Lennart Ante, Aman Saggu

The Ethereum blockchain network enables transaction processing and smart-contract execution through levies of transaction fees, commonly known as gas fees. This framework mediates economic participation via a market-based mechanism for gas fees, permitting users to offer higher gas fees to expedite pro-cessing. Historically, the ensuing gas fee volatility led to critical disequilibria between supply and demand for block space, presenting stakeholder challenges. This study examines the dynamic causal interplay between transaction fees and economic subsystems leveraging the network. By utilizing data related to unique active wallets and transaction volume of each subsystem and applying time-varying Granger causality analysis, we reveal temporal heterogeneity in causal relationships between economic activity and transaction fees across all subsystems. This includes (a) a bidirectional causal feedback loop between cross-blockchain bridge user activity and transaction fees, which diminishes over time, potentially signaling user migration; (b) a bidirectional relationship between centralized cryptocurrency exchange deposit and withdrawal transaction volume and fees, indicative of increased competition for block space; (c) decentralized exchange volumes causally influence fees, while fees causally influence user activity, although this relationship is weakening, potentially due to the diminished significance of decentralized finance; (d) intermittent causal relationships with maximal extractable value bots; (e) fees causally in-fluence non-fungible token transaction volumes; and (f) a highly significant and growing causal influence of transaction fees on stablecoin activity and transaction volumes highlight its prominence.

📄 PDF Abstract BibTeX arXiv:2501.05299

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Extracting Causal Relations in Deep Knowledge Tracing

2025-11-06 · Kevin Hong, Kia Karbasi, Gregory Pottie arxiv

A longstanding goal in computational educational research is to develop explainable knowledge tracing (KT) models. Deep Knowledge Tracing (DKT), which leverages a Recurrent Neural Network (RNN) to predict student knowled…

Knowledge Tracing

Causal Inference from Slowly Varying Nonstationary Processes

2020-12-23 · Kang Du, Yu Xiang

Causal inference from observational data following the restricted structural causal model (SCM) framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms, such as non-Gaussian…

Causal DiscoveryCausal IdentificationCausal InferenceTime Series+1

Causal Inference from Slowly Varying Nonstationary Processes

2024-05-11 · Kang Du, Yu Xiang

Causal inference from observational data following the restricted structural causal models (SCM) framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms, such as non-Gaussia…

Causal IdentificationCausal InferenceTime Series

Dynamic Causal Structure Discovery and Causal Effect Estimation

2025-01-11 · Jianian Wang, Rui Song

To represent the causal relationships between variables, a directed acyclic graph (DAG) is widely utilized in many areas, such as social sciences, epidemics, and genetics. Many causal structure learning approaches are de…

Causal Discovery

Transformers with Sparse Attention for Granger Causality

2024-11-20 · Riya Mahesh, Rahul Vashisht, Chandrashekar Lakshminarayanan

Temporal causal analysis means understanding the underlying causes behind observed variables over time. Deep learning based methods such as transformers are increasingly used to capture temporal dynamics and causal relat…