Causal Reasoning and Counterfactual Probabilistic Programming Framework Using Approximate Inference
A computer implemented method of performing inference on a generative model, wherein the generative model in a probabilistic program form, said probabilistic program form defining variables and probabilistic relationships between variables, the method comprising: providing at least one of observations or interventions to the generative model; selecting an inference method, wherein the inference method is selected from one of: observational inference, interventional inference or counterfactual inference; performing the selected inference method using an approximate inference method on the generative model; and outputting a predicted outcome from the results of the inference; wherein approximate inference is performed by inputting an inference query and the model, observations, interventions and inference query are provided as independent parameters such that they can be iterated over and varied independently of each other.
1 . A computer implemented method of performing inference on a generative model,
wherein the generative model in a probabilistic program form, said probabilistic program form defining variables and probabilistic relationships between variables, the method comprising:
providing at least one of observations or interventions to the generative model;
selecting an inference method, wherein the inference method is selected from one of: observational inference, interventional inference or counterfactual inference;
performing the selected inference method using an approximate inference method on the generative model; and
outputting a predicted outcome from the results of the inference,
wherein approximate inference is performed by inputting an inference query and the model, observations, interventions and inference query are provided as independent parameters such that they can be iterated over and varied independently of each other.
2 . A method according to claim 1 , wherein approximate inference is performed in an inference engine and each inference query is fully performed as just one single request to the inference engine.
3 . A method according to claim 1 , wherein the approximate inference method is importance sampling.
4 . A method according to claim 1 , wherein the generative model expresses noise as explicit random variables.
5 . A method according to claim 1 , wherein performing observational inference comprises:
retrieving said generative model; and
weighting the prior space of the generative model by the likelihood that is calculated on the observed probabilistic procedures.
6 . A method according to claim 1 , wherein performing interventional inference comprises:
retrieving said generative model;
representing said generative model with intervened variable; and
weighting the prior space of the generative model representation by the likelihood that is calculated on the observed probabilistic procedures.
7 . A method according to claim 1 , wherein performing counterfactual inference comprises:
retrieving said generative model;
weighting the prior space of the generative model by the likelihood that is calculated on the observed probabilistic procedures; and
performing intervention on a representation of that model and predicting the variables of interest.
8 . A method according to claim 7 , wherein during the weighting the prior space of the generative model by the likelihood that is calculated on the observed probabilistic procedures, the noise distributions of observed probabilistic procedures are represented.
9 . A method according to claim 2 , wherein the generative model is provided on a first server and the inference query is input at a location separate from the first server, wherein the inference query is sent as a single request from the said location to the first server and the server returns the result of the inference as a single message to the said location.
10 . A method according to claim 8 , wherein the result of the inference is the calculated inference query result.
11 . A method according to claim 9 , wherein the inference query is input via a mobile device.
12 . A method according to claim 1 , further comprising:
performing static analysis on the generative model, observations and interventions, given inference query types, to optimise the inference method.
13 . A method according to claim 1 , further comprising:
performing a dynamic analysis on the generative model, observations and interventions, given inference query types, to optimise the inference method.
14 . A system adapted to perform inference on a generative model,
the system comprising a processor and a memory, the generative model being stored in a probabilistic program form in said memory, said probabilistic program form defining variables and probabilistic relationships between variables, the processor being configured to:
provide at least one of observations or interventions to the generative model;
allow selection of an inference method, wherein the inference method is selected from one of: observational inference, interventional inference or counterfactual inference;
perform the selected inference method using an approximate inference method on the generative model; and
output a predicted outcome from the results of the inference;
wherein approximate inference is performed by inputting an inference query and the model, observations, interventions and inference query are provided as independent parameters such that they can be iterated over and varied independently of each other.
15 . A non-transitory computer medium carrying computer readable instructions that when run on a computer will cause the computer to perform the method of claim 1 .