, such as employer-employee or student-teacher panel data. We formalize how the structure of the network affects the accuracy …

, such as employer-employee or student-teacher panel data. We formalize how the structure of the network affects the accuracy …

, such as employer-employee or student-teacher panel data. We formalize how the structure of the network affects the accuracy …

. These bounds depend on the smallest non-zero eigenvalue of the (normalized) Laplacian of the network and on the degree … structure of the network. The Laplacian is a matrix that describes the network and its smallest non-zero eigenvalue is a measure … of connectivity, with smaller values indicating less-connected networks. These bounds yield conditions for consistent …

(normalized) Laplacian of the network and of the degree structure of the network. The eigenvalue is a measure of connectivity …

. These bounds depend on the smallest non-zero eigenvalue of the (normalized) Laplacian of the network and on the degree … structure of the network. The Laplacian is a matrix that describes the network and its smallest non-zero eigenvalue is a measure … of connectivity, with smaller values indicating less-connected networks. These bounds yield conditions for consistent …

(normalized) Laplacian of the network and of the degree structure of the network. The eigenvalue is a measure of connectivity …

We propose a new estimator for average causal effects of a binary treatment with panel data in settings with general …