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The simple answer is that you already do. Conventional DAGs do not only represent main effects but rather the combination of main effects and interactions. Once you have drawn your DAG, you already assume that any variables pointing to the same outcome can modify the effect of the others pointing to the same outcome. It is a modeling assumption, separate from the DAG, which presumes the lack of an interaction.

In addition, interaction can occur without including an explicit interaction term in your model. If you include main effects only in a model for the risk ratio of Y with respect to treatment T and covariate Q, the estimate of the risk difference will differ depending on the level of Q. In order to accommodate all these possibilities nonparametrically, DAGs make only the weakest assumptions on the functional form of the relationships among the variables, and assuming no interaction is a stronger assumption that allowing for an interaction. This again is to say that DAGs already allow for interaction without any adjustment. See Vanderweele (2009) for a discussion of interaction that uses conventional DAGs but allows for interaction.

Bollen & Paxton (1998) and Muthén & Asparouhov (2015) both demonstrate interactions in path models with latent variables, but these interactions explicitly refer to product terms in a parametric model rather than to interactions broadly. I have also seen diagrams similar to yours where the causal arrow points to a path, but strictly speaking a path is not a unique quantity that a variable can have a causal effect on (even though that may be how we want to interpret our models); it simply represents the presence of a causal effect, not its magnitude.

Bollen, K. A., & Paxton, P. (1998). Interactions of latent variables in structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 5(3), 267-293.

Muthén, B., & Asparouhov, T. (2015). Latent variable interactions.

VanderWeele, T. J. (2009). On the distinction between interaction and effect modification. Epidemiology, 20(6), 863-871.

The simple answer is that you already do. Conventional DAGs do not only represent main effects but rather the combination of main effects and interactions. Once you have drawn your DAG, you already assume that any variables pointing to the same outcome can modify the effect of the others pointing to the same outcome. It is a modeling assumption, separate from the DAG, which presumes the lack of an interaction.

In addition, interaction can occur without including an explicit interaction term in your model. If you include main effects only in a model for the risk ratio of Y with respect to treatment T and covariate Q, the estimate of the risk difference will differ depending on the level of Q. In order to accommodate all these possibilities nonparametrically, DAGs make only the weakest assumptions on the functional form of the relationships among the variables, and assuming no interaction is a stronger assumption that allowing for an interaction. This again is to say that DAGs already allow for interaction without any adjustment. See Vanderweele (2009) for a discussion of interaction that uses conventional DAGs but allows for interaction.

Bollen & Paxton (1998) and Muthén & Asparouhov (2015) both demonstrate interactions in path models with latent variables, but these interactions explicitly refer to product terms in a parametric model rather than to interactions broadly. I have also seen diagrams similar to yours where the causal arrow points to a path, but strictly speaking a path is not a unique quantity that a variable can have a causal effect on (even though that may be how we want to interpret our models); it simply represents the presence of a causal effect, not its magnitude.

Bollen, K. A., & Paxton, P. (1998). Interactions of latent variables in structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 5(3), 267-293.

Asparouhov, T. & Muthén, B. (2020): Bayesian estimation of single and multilevel models with latent variable interactions, Structural Equation Modeling: A Multidisciplinary Journal

VanderWeele, T. J. (2009). On the distinction between interaction and effect modification. Epidemiology, 20(6), 863-871.

The simple answer is that you already do. Conventional DAGs do not only represent main effects but rather the combination of main effects and interactions. Once you have drawn your DAG, you already assume that any variables pointing to the same outcome can modify the effect of the others pointing to the same outcome. It is a modeling assumption, separate from the DAG, which presumes the lack of an interaction.

In addition, interaction can occur without including an explicit interaction term in your model. If you include main effects only in a model for the risk ratio of Y with respect to treatment T and covariate Q, the estimate of the risk difference will differ depending on the level of Q. In order to accommodate all these possibilities nonparametrically, DAGs make only the weakest assumptions on the functional form of the relationships among the variables, and assuming no interaction is a stronger assumption that allowing for an interaction. This again is to say that DAGs already allow for interaction without any adjustment. See Vanderweele (2009) for a discussion of interaction that uses conventional DAGs but allows for interaction.

Bollen & Paxton (1998) and Muthén & Asparouhov (2015) both demonstrate interactions in path models with latent variables, but these interactions explicitly refer to product terms in a parametric model rather than to interactions broadly. I have also seen diagrams similar to yours where the causal arrow points to a path, but strictly speaking a path is not a unique quantity that a variable can have a causal effect on (even though that may be how we want to interpret our models); it simply represents the presence of a causal effect, not its magnitude.

Bollen, K. A., & Paxton, P. (1998). Interactions of latent variables in structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 5(3), 267-293.

Muthén, B., & Asparouhov, T. (2015). Latent variable interactions.

VanderWeele, T. J. (2009). On the distinction between interaction and effect modification. Epidemiology, 20(6), 863-871.

The simple answer is that you already do. Conventional DAGs do not only represent main effects but rather the combination of main effects and interactions. Once you have drawn your DAG, you already assume that any variables pointing to the same outcome can modify the effect of the others pointing to the same outcome. It is a modeling assumption, separate from the DAG, which presumes the lack of an interaction.

In addition, interaction can occur without including an explicit interaction term in your model. If you include main effects only in a model for the risk ratio of Y with respect to treatment T and covariate Q, the estimate of the risk difference will differ depending on the level of Q. In order to accommodate all these possibilities nonparametrically, DAGs make only the weakest assumptions on the functional form of the relationships among the variables, and assuming no interaction is a stronger assumption that allowing for an interaction. This again is to say that DAGs already allow for interaction without any adjustment. See Vanderweele (2009) for a discussion of interaction that uses conventional DAGs but allows for interaction.

Bollen & Paxton (1998) and Muthén & Asparouhov (2015) both demonstrate interactions in path models with latent variables, but these interactions explicitly refer to product terms in a parametric model rather than to interactions broadly. I have also seen diagrams similar to yours where the causal arrow points to a path, but strictly speaking a path is not a unique quantity that a variable can have a causal effect on (even though that may be how we want to interpret our models); it simply represents the presence of a causal effect, not its magnitude.

Bollen, K. A., & Paxton, P. (1998). Interactions of latent variables in structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 5(3), 267-293.

Muthén, B., & Asparouhov, T. (2015). Latent variable interactions.

VanderWeele, T. J. (2009). On the distinction between interaction and effect modification. Epidemiology, 20(6), 863-871.

The simple answer is that you already do. Conventional DAGs do not only represent main effects but rather the combination of main effects and interactions. Once you have drawn your DAG, you already assume that any variables pointing to the same outcome can modify the effect of the others pointing to the same outcome. It is a modeling assumption, separate from the DAG, which presumes the lack of an interaction.

In addition, interaction can occur without including an explicit interaction term in your model. If you include main effects only in a model for the risk ratio of Y with respect to treatment T and covariate Q, the estimate of the risk difference will differ depending on the level of Q. In order to accommodate all these possibilities nonparametrically, DAGs make only the weakest assumptions on the functional form of the relationships among the variables, and assuming no interaction is a stronger assumption that allowing for an interaction. This again is to say that DAGs already allow for interaction without any adjustment. See Vanderweele (2009) for a discussion of interaction that uses conventional DAGs but allows for interaction.

Bollen & Paxton (1998) and Muthen & Asperouhov (2015) both demonstrate interactions in path models with latent variables, but these interactions explicitly refer to product terms in a parametric model rather than to interactions broadly. I have also seen diagrams similar to yours where the causal arrow points to a path, but strictly speaking a path is not a unique quantity that a variable can have a causal effect on (even though that may be how we want to interpret our models); it simply represents the presence of a causal effect, not its magnitude.

The simple answer is that you already do. Conventional DAGs do not only represent main effects but rather the combination of main effects and interactions. Once you have drawn your DAG, you already assume that any variables pointing to the same outcome can modify the effect of the others pointing to the same outcome. It is a modeling assumption, separate from the DAG, which presumes the lack of an interaction.

In addition, interaction can occur without including an explicit interaction term in your model. If you include main effects only in a model for the risk ratio of Y with respect to treatment T and covariate Q, the estimate of the risk difference will differ depending on the level of Q. In order to accommodate all these possibilities nonparametrically, DAGs make only the weakest assumptions on the functional form of the relationships among the variables, and assuming no interaction is a stronger assumption that allowing for an interaction. This again is to say that DAGs already allow for interaction without any adjustment. See Vanderweele (2009) for a discussion of interaction that uses conventional DAGs but allows for interaction.

Bollen & Paxton (1998) and Muthén & Asparouhov (2015) both demonstrate interactions in path models with latent variables, but these interactions explicitly refer to product terms in a parametric model rather than to interactions broadly. I have also seen diagrams similar to yours where the causal arrow points to a path, but strictly speaking a path is not a unique quantity that a variable can have a causal effect on (even though that may be how we want to interpret our models); it simply represents the presence of a causal effect, not its magnitude.

Bollen, K. A., & Paxton, P. (1998). Interactions of latent variables in structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 5(3), 267-293.

Muthén, B., & Asparouhov, T. (2015). Latent variable interactions.

VanderWeele, T. J. (2009). On the distinction between interaction and effect modification. Epidemiology, 20(6), 863-871.