This paper concerns the assessment of direct causal effects from a combination of: (i) non-experimental data, and (ii) qualitative domain knowledge. Domain knowledge is encoded in the form of a directed acyclic graph (DAG), in which all interactions are assumed linear, and some variables are presumed to be unobserved. We provide a generalization of the well-known method of Instrumental Variables, which makes allows its application to models with few conditional independencies
Learning a causal effect from observational data requires strong assumptions. One possible method is...
Abstract Instrumental Variables are a popular way to identify the direct causal effect of a random v...
<p>Instrumental variable models use associations C and A to estimate the causal effect of a risk fac...
This paper concerns the assessment of direct causal effects from a combination of: (i) non-experimen...
This paper concerns the assessment of direct causal effects from a combination of: (i) non-experimen...
This paper concerns the assessment of direct causal effects from a combination of: (i) non-experimen...
This paper builds on the structural equations, treatment effect, and machine learning literatures to...
The instrumental variable (IV) approach is a widely used way to estimate the causal effects of a tre...
This paper concerns the assessment of the effects of actions or policy interventions from a combina...
Instrumental variables (IVs) are widely used to identify causal effects. For this purpose IVs have t...
Abstract: This paper builds on the structural equations, treatment effect, and machine learning lite...
This paper concerns the assessment of the effects of actions or policy interventions from a combinat...
Learning a causal effect from observational data is not straightforward, as this is not possible wit...
This paper concerns the assessment of the effects of actions from a combination of nonexperimental d...
Instrumental variables have been used for a long time in the econometrics literature for the identif...
Learning a causal effect from observational data requires strong assumptions. One possible method is...
Abstract Instrumental Variables are a popular way to identify the direct causal effect of a random v...
<p>Instrumental variable models use associations C and A to estimate the causal effect of a risk fac...
This paper concerns the assessment of direct causal effects from a combination of: (i) non-experimen...
This paper concerns the assessment of direct causal effects from a combination of: (i) non-experimen...
This paper concerns the assessment of direct causal effects from a combination of: (i) non-experimen...
This paper builds on the structural equations, treatment effect, and machine learning literatures to...
The instrumental variable (IV) approach is a widely used way to estimate the causal effects of a tre...
This paper concerns the assessment of the effects of actions or policy interventions from a combina...
Instrumental variables (IVs) are widely used to identify causal effects. For this purpose IVs have t...
Abstract: This paper builds on the structural equations, treatment effect, and machine learning lite...
This paper concerns the assessment of the effects of actions or policy interventions from a combinat...
Learning a causal effect from observational data is not straightforward, as this is not possible wit...
This paper concerns the assessment of the effects of actions from a combination of nonexperimental d...
Instrumental variables have been used for a long time in the econometrics literature for the identif...
Learning a causal effect from observational data requires strong assumptions. One possible method is...
Abstract Instrumental Variables are a popular way to identify the direct causal effect of a random v...
<p>Instrumental variable models use associations C and A to estimate the causal effect of a risk fac...