For example, to study the causal relations between the drugs and diseases, one can conduct clinical tests via selectively administering the medicines to the patients.

Thus, becoming convoluted and difficult to analyze.

Our focus in this paper is to solve causal inference problems using only observational data and (potentially partial) knowledge on the causal graph of the underlying structural causal model (SCM).

The PCMCI algorithm is proposed by Runge et al. [2019], aiming to detect time-lagged causal See Fig.1 for more detail. A simple proof is shown below through Markov assumption ( A2). 3 Figure 2: Partial causal graph for 3-variate time series Fig.2 shows a partial causal graph for a 3-variate time series with Semi-Stationary SCM. However, they may not share the same marginal distribution. Still in Fig.2, based on the definition of homogenous time partition, time partition subset Based on Eq.(12) and Eq.(17), we have: p(X Without loss of generality, we assume T is a multiple of δ all the time. A1-A7 and with an oracle (infinite sample size limit), we have that: null G = G (47) almost surely.

Discovering causal relations from observational time series without making the stationary assumption is a significant challenge.

A unifying theme in the design of intelligent agents is to efficiently optimize a policy based on what prior knowledge of the problem is available and what actions can be taken to learn more about it.

However, the exact formula for front-door adjustment depends on the structure of the graph, which is difficult to learn in practice.