v0.7: Causal discovery, ID identification, and faster backdoor identification

• [Major] Faster backdoor identification with support for minimal adjustment, maximal adjustment
  or exhaustive search. More test coverage for identification.

• [Major] Added new functionality of causal discovery [Experimental].
  DoWhy now supports discovery algorithms from external libraries like CDT.
  Example notebook

• [Major] Implemented ID algorithm for causal identification. [Experimental]

• Added friendly text-based interpretation for DoWhy's effect estimate.

• Added a new estimation method, distance matching that relies on a distance
  metrics between inputs.

• Heuristics to infer default parameters for refuters.

• Inferring default strata automatically for propensity score stratification.

• Added support for custom propensity models in propensity-based estimation
  methods.

• Bug fixes for confidence intervals for linear regression. Better version of
  bootstrap method.

• Allow effect estimation without need to refit the model for econml estimators

Big thanks to @AndrewC19, @ha2trinh, @siddhanthaldar, and @vojavocni