Making Causal Discovery work in real-world business settings | by Ryan O’Sullivan | Mar, 2024

11 minutes, 23 seconds Read

Causal AI, exploring the combination of causal reasoning into machine studying

Welcome to my sequence on Causal AI, the place we are going to discover the combination of causal reasoning into machine studying fashions. Anticipate to discover a lot of sensible functions throughout totally different enterprise contexts.

Within the first article we explored utilizing Causal Graphs to reply causal questions. This time spherical we are going to delve into making Causal Discovery work in real-world enterprise settings.

If you happen to missed the primary article on Causal Graphs, test it out right here:

This text goals that will help you navigate the world of causal discovery.

It’s geared toward anybody who needs to know extra about:

What causal discovery is, together with what assumptions it makes.A deep dive into conditional independence exams, the constructing blocks of causal discovery.An outline of the PC algorithm, a preferred causal discovery algorithm.A labored case research in Python illustrating the best way to apply the PC algorithm.Steerage on making causal discovery work in real-world enterprise settings.

The complete pocket book will be discovered right here:

In my final article, I lined how causal graphs could possibly be used to reply causal questions.

Sometimes called a DAG (directed acyclic graph), a causal graph incorporates nodes and edges — Edges hyperlink nodes that causally associated.

There are two methods to find out a causal graph:

Skilled area knowledgeCausal discovery algorithms

We don’t all the time have the skilled area information to find out a causal graph. On this pocket book we are going to discover the best way to take observational information and decide the causal graph utilizing causal discovery algorithms.

Causal discovery is a closely researched space in academia with 4 teams of strategies proposed:

It isn’t clear from at the moment obtainable analysis which methodology is finest. One of many challenges in answering this query is the dearth of sensible floor fact benchmark datasets.

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On this weblog we’re going to concentrate on understanding the PC algorithm, a constraint-based methodology that makes use of conditional independence exams.

Earlier than we introduce the PC algorithm, let’s cowl the important thing assumptions made by causal discovery algorithms:

Causal Markov Situation: Every variable is conditionally unbiased of its non-descendants, given its direct causes. This tells us that if we all know the causes of a variable, we don’t acquire any extra energy by understanding the variables that aren’t straight influenced by these causes. This elementary assumption simplifies the modelling of causal relationships enabling us to make causal inferences.Causal Faithfulness: If statistical independence holds within the information, then there isn’t a direct causal relationships between the corresponding variables. Testing this assumption is difficult and violations might point out mannequin misspecification or lacking variables.Causal Sufficiency: Are the variables included ample to make causal claims concerning the variables of curiosity? In different phrases, we want all confounders of the variables included to be noticed. Testing this assumption includes sensitivity evaluation which assesses the affect of probably unobserved confounders.Acyclicity: No cycles within the graph.

In apply, whereas these assumptions are mandatory for causal discovery, they’re usually handled as assumptions quite than straight examined.

Even with making these assumptions, we will find yourself with a Markov equivalence class. Now we have a Markov equivalence class when we’ve got a number of causal graphs every as doubtless as one another.

Conditional independence exams are the constructing blocks of causal discovery and are utilized by the PC algorithm (which we are going to cowl shortly).

Let’s begin by understanding independence. Independence between two variables implies that understanding the worth of 1 variable supplies no details about the worth of the opposite. On this case, it’s pretty protected to imagine that neither straight causes the opposite. Nonetheless, if two variables aren’t unbiased, it will be unsuitable to blindly assume causation.

Conditional independence exams can be utilized to find out whether or not two variables are unbiased of one another given the presence of a number of different variables. If two variables are conditionally unbiased, we will then infer that they don’t seem to be causally associated.

The Fisher’s actual take a look at can be utilized to find out if there’s a vital affiliation between two variables while controlling for the consequences of a number of extra variables (use the extra variables to separate the info into subsets, the take a look at can then be utilized to every subset of knowledge). The null speculation assumes that there isn’t a affiliation between the 2 variables of curiosity. A p-value can then be calculated and whether it is beneath 0.05 the null speculation will likely be rejected suggesting that there’s vital affiliation between the variables.

We are able to use an instance of a spurious correlation as an example the best way to use conditional independence exams.

Two variables have a spurious correlation after they have a standard trigger e.g. Excessive temperatures growing the variety of ice cream gross sales and shark assaults.


# Create dataset with spurious correlationtemperature = np.random.regular(loc=0, scale=1, dimension=1000)ice_cream_sales = 2.5 * temperature + np.random.regular(loc=0, scale=1, dimension=1000)shark_attacks = 0.5 * temperature + np.random.regular(loc=0, scale=1, dimension=1000)df_spurious = pd.DataFrame(information=dict(temperature=temperature, ice_cream_sales=ice_cream_sales, shark_attacks=shark_attacks))

# Pairplotsns.pairplot(df_spurious, nook=True)

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# Create node lookup variablesnode_lookup = 0: ‘Temperature’,1: ‘Ice cream gross sales’,2: ‘Shark assaults’

total_nodes = len(node_lookup)

# Create adjacency matrix – that is the bottom for our graphgraph_actual = np.zeros((total_nodes, total_nodes))

# Create graph utilizing skilled area knowledgegraph_actual[0, 1] = 1.0 # Temperature -> Ice cream salesgraph_actual[0, 2] = 1.0 # Temperature -> Shark assaults

plot_graph(input_graph=graph_actual, node_lookup=node_lookup)

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The next conditional independence exams can be utilized to find out the causal graph:

# Run first conditional independence testtest_id_1 = spherical(gcm.independence_test(ice_cream_sales, shark_attacks, conditioned_on=temperature), 2)

# Run second conditional independence testtest_id_2 = spherical(gcm.independence_test(ice_cream_sales, temperature, conditioned_on=shark_attacks), 2)

# Run third conditional independence testtest_id_3 = spherical(gcm.independence_test(shark_attacks, temperature, conditioned_on=ice_cream_sales), 2)

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Though we don’t know the course of the relationships, we will accurately infer that temperature is causally associated to each ice cream gross sales and shark assaults.

The PC algorithm (named after its inventors Peter and Clark) is a constraint-based causal discovery algorithm that makes use of conditional independence exams.

It may be summarised into 2 fundamental steps:

It begins with a completely related graph after which makes use of conditional independence exams to take away edges and establish the undirected causal graph (nodes linked however with no course).It then (partially) directs the sides utilizing numerous orientation tips.

We are able to use the earlier spurious correlation instance as an example step one:

Begin with a completely related graphTest ID 1: Settle for the null speculation and delete edge, no causal hyperlink between ice cream gross sales and shark attacksTest ID 2: Reject the null speculation and preserve the sting, causal hyperlink between ice cream gross sales and temperatureTest ID 3: Reject the null speculation and preserve the sting, causal hyperlink between shark assaults and ice cream gross sales

One of many key challenges in causal discovery is evaluating the outcomes. If we knew the causal graph, we wouldn’t want to use a causal discovery algorithm! Nonetheless, we will create artificial datasets to judge how nicely causal discovery algorithms carry out.

There are a number of metrics we will use to judge causal discovery algorithms:

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True positives: Determine causal hyperlink correctlyFalse positives: Determine causal hyperlink incorrectlyTrue negatives: Accurately establish no causal linkFalse negatives: Incorrectly establish no causal linkReversed edges: Determine causal hyperlink accurately however within the unsuitable course

We wish a excessive variety of True positives, however this shouldn’t be on the expense of a excessive variety of False positives (as after we come to construct an SCM, unsuitable causal hyperlinks will be very damaging). Subsequently GScore appears to seize this nicely while giving an interpretable ratio between 0 and 1.

We are going to revisit the decision centre case research from my earlier article. Initially, we decide the causal graph (for use as floor fact) after which use our information of the data-generating course of to create some samples.

The bottom fact causal graph and generated samples will allow us to judge the PC algorithm.

# Create node lookup for channelsnode_lookup = 0: ‘Demand’,1: ‘Name ready time’,2: ‘Name deserted’, 3: ‘Reported issues’, 4: ‘Low cost despatched’,5: ‘Churn’

total_nodes = len(node_lookup)

# Create adjacency matrix – that is the bottom for our graphgraph_actual = np.zeros((total_nodes, total_nodes))

# Create graph utilizing skilled area knowledgegraph_actual[0, 1] = 1.0 # Demand -> Name ready timegraph_actual[0, 2] = 1.0 # Demand -> Name abandonedgraph_actual[0, 3] = 1.0 # Demand -> Reported problemsgraph_actual[1, 2] = 1.0 # Name ready time -> Name abandonedgraph_actual[1, 5] = 1.0 # Name ready time -> Churngraph_actual[2, 3] = 1.0 # Name deserted -> Reported problemsgraph_actual[2, 5] = 1.0 # Name deserted -> Churngraph_actual[3, 4] = 1.0 # Reported issues -> Low cost sentgraph_actual[3, 5] = 1.0 # Reported issues -> Churngraph_actual[4, 5] = 1.0 # Low cost despatched -> Churn

plot_graph(input_graph=graph_actual, node_lookup=node_lookup)

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def data_generator(max_call_waiting, inbound_calls, call_reduction):”’An information producing perform that has the flexibleness to cut back the worth of node 0 (Name ready time) – this permits us to calculate floor fact counterfactuals

Args:max_call_waiting (int): Most name ready time in secondsinbound_calls (int): Complete variety of inbound calls (observations in information)call_reduction (float): Discount to use to name ready time

Returns:DataFrame: Generated information”’

df = pd.DataFrame(columns=node_lookup.values())

df[node_lookup[0]] = np.random.randint(low=10, excessive=max_call_waiting, dimension=(inbound_calls)) # Demanddf[node_lookup[1]] = (df[node_lookup[0]] * 0.5) * (call_reduction) + np.random.regular(loc=0, scale=40, dimension=inbound_calls) # Name ready timedf[node_lookup[2]] = (df[node_lookup[1]] * 0.5) + (df[node_lookup[0]] * 0.2) + np.random.regular(loc=0, scale=30, dimension=inbound_calls) # Name abandoneddf[node_lookup[3]] = (df[node_lookup[2]] * 0.6) + (df[node_lookup[0]] * 0.3) + np.random.regular(loc=0, scale=20, dimension=inbound_calls) # Reported problemsdf[node_lookup[4]] = (df[node_lookup[3]] * 0.7) + np.random.regular(loc=0, scale=10, dimension=inbound_calls) # Low cost sentdf[node_lookup[5]] = (0.10 * df[node_lookup[1]] ) + (0.30 * df[node_lookup[2]]) + (0.15 * df[node_lookup[3]]) + (-0.20 * df[node_lookup[4]]) # Churn

return df

# Generate datanp.random.seed(999)df = data_generator(max_call_waiting=600, inbound_calls=10000, call_reduction=1.00)

# Pairplotsns.pairplot(df, nook=True)

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The Python bundle gCastle has a number of causal discovery algorithms applied, together with the PC algorithm:

Once we feed the algorithm our samples we obtain again the discovered causal graph (within the type of an adjacency matrix).

# Apply PC methodology to be taught graphpc = PC(variant=’secure’) taught(df)graph_pred = laptop.causal_matrix


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gCastle additionally has a number of analysis metrics obtainable, together with gScore. The GScore of our discovered graph is 0! Why has it achieved so poorly?

# GScoremetrics = MetricsDAG(B_est=graph_pred, B_true=graph_actual)metrics.metrics[‘gscore’]

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On nearer inspection of the discovered graph, we will see that it accurately recognized the undirected graph after which struggled to orient the sides.

plot_graph(input_graph=graph_pred, node_lookup=node_lookup)

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To construct on the training from making use of the PC algorithm, we will use gCastle to extract the undirected causal graph that was discovered.

# Apply PC methodology to be taught skeletonskeleton_pred, sep_set = find_skeleton(df.to_numpy(), 0.05, ‘fisherz’)


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If we rework our floor fact graph into an undirected adjacency matrix, we will then use it to calculate the Gscore of the undirected graph.

# Remodel the bottom fact graph into an undirected adjacency matrixskeleton_actual = graph_actual + graph_actual.Tskeleton_actual = np.the place(skeleton_actual > 0, 1, 0)

Utilizing the discovered undirected causal graph we get a GScore of 1.00.

# GScoremetrics = MetricsDAG(B_est=skeleton_pred, B_true=skeleton_actual)metrics.metrics[‘gscore’]

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Now we have precisely discovered an undirected graph — may we use skilled area information to direct the sides? The reply to this can differ throughout totally different use instances, however it’s a cheap technique.

plot_graph(input_graph=skeleton_pred, node_lookup=node_lookup)

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We have to begin seeing causal discovery as an important EDA step in any causal inference challenge:

Nonetheless, we additionally must be clear about its limitations.Causal discovery is a software that wants complementing with skilled area information.

Be pragmatic with the assumptions:

Can we ever count on to look at all confounders? Most likely not. Nonetheless, with the proper area information and in depth information gathering, it’s possible that we may observe all the important thing confounders.

Choose an algorithm the place we will apply constraints to include skilled area information — gCastle permits us to use constraints to the PC algorithm:

Initially work on figuring out the undirected causal graph after which share this output with area consultants and use them to assist orient the graph.

Be cautious when utilizing proxy variables and take into account imposing constraints on relationships we strongly consider exist:

For instance, if embrace Google traits information as a proxy for product demand, we might have to implement constraints by way of this driving gross sales.What if we’ve got non-linear relationships? Can the PC algorithm deal with this?What occurs if we’ve got unobserved confounders? Can the FCI algorithm take care of this case successfully?How do constraint-based, score-based, functional-based and gradient-based strategies evaluate?

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