Inference with cross-lagged effects-Problems in time
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View abstract on PubMed
Summary
This summary is machine-generated.Discrete-time models can misinterpret continuous processes, affecting causal inference. Continuous-time modeling using stochastic differential equations offers a more accurate approach to understanding dynamic systems.
Area Of Science
- Quantitative Psychology
- Statistical Modeling
- Causal Inference
Background
- Vector autoregressive (VAR) models are widely used to infer causality from cross-effects.
- Interpreting these cross-effects can be problematic when continuous processes are modeled discretely.
Purpose Of The Study
- To highlight the interpretive issues of discrete-time models for continuous processes.
- To propose and demonstrate continuous-time modeling as a solution for accurate causal inference.
Main Methods
- Simulations were used to demonstrate problems with discrete-time models.
- Stochastic differential equations (SDEs) were parameterized for continuous-time inference.
- An empirical example with intensive longitudinal data was analyzed.
Main Results
- Discrete-time models can inaccurately represent continuous processes, leading to misinterpretations of causal effects.
- Continuous-time models reveal denser effect matrices than expected from discrete models.
- Switching to continuous-time modeling requires careful consideration of regularization, time lag, and model order.
Conclusions
- Continuous-time modeling provides a more accurate framework for inferring causality from dynamic systems.
- Proper model specification and parameter interpretation are crucial when applying continuous-time approaches to real-world data.
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