Name
Abstract | ||
|---|---|---|
We present an algorithm for estimating bounds on causal effects from observational data which combines graphical model search with simple linear regression. We assume that the underlying system can be represented by a linear structural equation model with no feedback, and we allow for the possibility of latent variables. Under assumptions standard in the causal search literature, we use conditional independence constraints to search for an equivalence class of ancestral graphs. Then, for each model in the equivalence class, we perform the appropriate regression (using causal structure information to determine which covariates to include in the regression) to estimate a set of possible causal effects. Our approach is based on the "IDA" procedure of Maathuis et al. (2009), which assumes that all relevant variables have been measured (i.e., no unmeasured confounders). We generalize their work by relaxing this assumption, which is often violated in applied contexts. We validate the performance of our algorithm on simulated data and demonstrate improved precision over IDA when latent variables are present. |
Year | Venue | Keywords |
|---|---|---|
2016 | Probabilistic Graphical Models | Causal inference,Markov equivalence,ancestral graphs,latent variables |
Field | DocType | Volume |
Applied mathematics,Causal structure,Computer science,Markov model,Conditional independence,Latent variable,Artificial intelligence,Simple linear regression,Variable-order Markov model,Graphical model,Causal Markov condition,Machine learning | Conference | 52 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Malinsky | 1 | 4 | 2.85 |
| Peter Spirtes | 2 | 616 | 101.07 |